diff --git a/README.md b/README.md
index e945edf4..7fd68e2f 100644
--- a/README.md
+++ b/README.md
@@ -1,1232 +1,1267 @@
-
-
-Strands Agents Tools is a community-driven project that provides a powerful set of tools for your agents to use. It bridges the gap between large language models and practical applications by offering ready-to-use tools for file operations, system execution, API interactions, mathematical operations, and more.
-
-## ✨ Features
-
-- 📁 **File Operations** - Read, write, and edit files with syntax highlighting and intelligent modifications
-- 🖥️ **Shell Integration** - Execute and interact with shell commands securely
-- 🧠 **Memory** - Store user and agent memories across agent runs to provide personalized experiences with both Mem0, Amazon Bedrock Knowledge Bases, Elasticsearch, and MongoDB Atlas
-- 🕸️ **Web Infrastructure** - Perform web searches, extract page content, and crawl websites with Tavily and Exa-powered tools
-- 🌐 **HTTP Client** - Make API requests with comprehensive authentication support
-- 💬 **Slack Client** - Real-time Slack events, message processing, and Slack API access
-- 🐍 **Python Execution** - Run Python code snippets with state persistence, user confirmation for code execution, and safety features
-- 🧮 **Mathematical Tools** - Perform advanced calculations with symbolic math capabilities
-- ☁️ **AWS Integration** - Seamless access to AWS services
-- 🖼️ **Image Processing** - Generate and process images for AI applications
-- 🎥 **Video Processing** - Use models and agents to generate dynamic videos
-- 🎙️ **Audio Output** - Enable models to generate audio and speak
-- 🔄 **Environment Management** - Handle environment variables safely
-- 📝 **Journaling** - Create and manage structured logs and journals
-- ⏱️ **Task Scheduling** - Schedule and manage cron jobs
-- 🧠 **Advanced Reasoning** - Tools for complex thinking and reasoning capabilities
-- 🐝 **Swarm Intelligence** - Coordinate multiple AI agents for parallel problem solving with shared memory
-- 🔌 **Dynamic MCP Client** - ⚠️ Dynamically connect to external MCP servers and load remote tools (use with caution - see security warnings)
-- 🔄 **Multiple tools in Parallel** - Call multiple other tools at the same time in parallel with Batch Tool
-- 🔍 **Browser Tool** - Tool giving an agent access to perform automated actions on a browser (chromium)
-- 📈 **Diagram** - Create AWS cloud diagrams, basic diagrams, or UML diagrams using python libraries
-- 📰 **RSS Feed Manager** - Subscribe, fetch, and process RSS feeds with content filtering and persistent storage
-- 🖱️ **Computer Tool** - Automate desktop actions including mouse movements, keyboard input, screenshots, and application management
-
-## 📦 Installation
-
-### Quick Install
-
-```bash
-pip install strands-agents-tools
-```
-
-To install the dependencies for optional tools:
-
-```bash
-pip install strands-agents-tools[mem0_memory, use_browser, rss, use_computer]
-```
-
-### Development Install
-
-```bash
-# Clone the repository
-git clone https://github.com/strands-agents/tools.git
-cd tools
-
-# Create and activate virtual environment
-python3 -m venv .venv
-source .venv/bin/activate # On Windows: venv\Scripts\activate
-
-# Install in development mode
-pip install -e ".[dev]"
-
-# Install pre-commit hooks
-pre-commit install
-```
-
-### Tools Overview
-
-Below is a comprehensive table of all available tools, how to use them with an agent, and typical use cases:
-
-| Tool | Agent Usage | Use Case |
-|------|-------------|----------|
-| a2a_client | `provider = A2AClientToolProvider(known_agent_urls=["http://localhost:9000"]); agent = Agent(tools=provider.tools)` | Discover and communicate with A2A-compliant agents, send messages between agents |
-| file_read | `agent.tool.file_read(path="path/to/file.txt")` | Reading configuration files, parsing code files, loading datasets |
-| file_write | `agent.tool.file_write(path="path/to/file.txt", content="file content")` | Writing results to files, creating new files, saving output data |
-| editor | `agent.tool.editor(command="view", path="path/to/file.py")` | Advanced file operations like syntax highlighting, pattern replacement, and multi-file edits |
-| shell* | `agent.tool.shell(command="ls -la")` | Executing shell commands, interacting with the operating system, running scripts |
-| http_request | `agent.tool.http_request(method="GET", url="https://api.example.com/data")` | Making API calls, fetching web data, sending data to external services |
-| tavily_search | `agent.tool.tavily_search(query="What is artificial intelligence?", search_depth="advanced")` | Real-time web search optimized for AI agents with a variety of custom parameters |
-| tavily_extract | `agent.tool.tavily_extract(urls=["www.tavily.com"], extract_depth="advanced")` | Extract clean, structured content from web pages with advanced processing and noise removal |
-| tavily_crawl | `agent.tool.tavily_crawl(url="www.tavily.com", max_depth=2, instructions="Find API docs")` | Crawl websites intelligently starting from a base URL with filtering and extraction |
-| tavily_map | `agent.tool.tavily_map(url="www.tavily.com", max_depth=2, instructions="Find all pages")` | Map website structure and discover URLs starting from a base URL without content extraction |
-| exa_search | `agent.tool.exa_search(query="Best project management tools", text=True)` | Intelligent web search with auto mode (default) that combines neural and keyword search for optimal results |
-| exa_get_contents | `agent.tool.exa_get_contents(urls=["https://example.com/article"], text=True, summary={"query": "key points"})` | Extract full content and summaries from specific URLs with live crawling fallback |
-| python_repl* | `agent.tool.python_repl(code="import pandas as pd\ndf = pd.read_csv('data.csv')\nprint(df.head())")` | Running Python code snippets, data analysis, executing complex logic with user confirmation for security |
+
+
+Strands Agents Tools is a community-driven project that provides a powerful set of tools for your agents to use. It bridges the gap between large language models and practical applications by offering ready-to-use tools for file operations, system execution, API interactions, mathematical operations, and more.
+
+## ✨ Features
+
+- 📁 **File Operations** - Read, write, and edit files with syntax highlighting and intelligent modifications
+- 🖥️ **Shell Integration** - Execute and interact with shell commands securely
+- 🧠 **Memory** - Store user and agent memories across agent runs to provide personalized experiences with both Mem0, Amazon Bedrock Knowledge Bases, Elasticsearch, and MongoDB Atlas
+- 🕸️ **Web Infrastructure** - Perform web searches, extract page content, and crawl websites with Tavily and Exa-powered tools
+- 🌐 **HTTP Client** - Make API requests with comprehensive authentication support
+- 💬 **Slack Client** - Real-time Slack events, message processing, and Slack API access
+- 🐍 **Python Execution** - Run Python code snippets with state persistence, user confirmation for code execution, and safety features
+- 🧮 **Mathematical Tools** - Perform advanced calculations with symbolic math capabilities
+- ☁️ **AWS Integration** - Seamless access to AWS services
+- 🖼️ **Image Processing** - Generate and process images for AI applications
+- 🎥 **Video Processing** - Use models and agents to generate dynamic videos
+- 🎙️ **Audio Output** - Enable models to generate audio and speak
+- 🔄 **Environment Management** - Handle environment variables safely
+- 📝 **Journaling** - Create and manage structured logs and journals
+- ⏱️ **Task Scheduling** - Schedule and manage cron jobs
+- 🧠 **Advanced Reasoning** - Tools for complex thinking and reasoning capabilities
+- 🐝 **Swarm Intelligence** - Coordinate multiple AI agents for parallel problem solving with shared memory
+- 🔌 **Dynamic MCP Client** - ⚠️ Dynamically connect to external MCP servers and load remote tools (use with caution - see security warnings)
+- 🔄 **Multiple tools in Parallel** - Call multiple other tools at the same time in parallel with Batch Tool
+- 🔍 **Browser Tool** - Tool giving an agent access to perform automated actions on a browser (chromium)
+- 📈 **Diagram** - Create AWS cloud diagrams, basic diagrams, or UML diagrams using python libraries
+- 📰 **RSS Feed Manager** - Subscribe, fetch, and process RSS feeds with content filtering and persistent storage
+- 🖱️ **Computer Tool** - Automate desktop actions including mouse movements, keyboard input, screenshots, and application management
+
+## 📦 Installation
+
+### Quick Install
+
+```bash
+pip install strands-agents-tools
+```
+
+To install the dependencies for optional tools:
+
+```bash
+pip install strands-agents-tools[mem0_memory, use_browser, rss, use_computer]
+```
+
+### Development Install
+
+```bash
+# Clone the repository
+git clone https://github.com/strands-agents/tools.git
+cd tools
+
+# Create and activate virtual environment
+python3 -m venv .venv
+source .venv/bin/activate # On Windows: venv\Scripts\activate
+
+# Install in development mode
+pip install -e ".[dev]"
+
+# Install pre-commit hooks
+pre-commit install
+```
+
+### Tools Overview
+
+Below is a comprehensive table of all available tools, how to use them with an agent, and typical use cases:
+
+| Tool | Agent Usage | Use Case |
+|------|-------------|----------|
+| a2a_client | `provider = A2AClientToolProvider(known_agent_urls=["http://localhost:9000"]); agent = Agent(tools=provider.tools)` | Discover and communicate with A2A-compliant agents, send messages between agents |
+| file_read | `agent.tool.file_read(path="path/to/file.txt")` | Reading configuration files, parsing code files, loading datasets |
+| file_write | `agent.tool.file_write(path="path/to/file.txt", content="file content")` | Writing results to files, creating new files, saving output data |
+| editor | `agent.tool.editor(command="view", path="path/to/file.py")` | Advanced file operations like syntax highlighting, pattern replacement, and multi-file edits |
+| shell* | `agent.tool.shell(command="ls -la")` | Executing shell commands, interacting with the operating system, running scripts |
+| http_request | `agent.tool.http_request(method="GET", url="https://api.example.com/data")` | Making API calls, fetching web data, sending data to external services |
+| tavily_search | `agent.tool.tavily_search(query="What is artificial intelligence?", search_depth="advanced")` | Real-time web search optimized for AI agents with a variety of custom parameters |
+| tavily_extract | `agent.tool.tavily_extract(urls=["www.tavily.com"], extract_depth="advanced")` | Extract clean, structured content from web pages with advanced processing and noise removal |
+| tavily_crawl | `agent.tool.tavily_crawl(url="www.tavily.com", max_depth=2, instructions="Find API docs")` | Crawl websites intelligently starting from a base URL with filtering and extraction |
+| tavily_map | `agent.tool.tavily_map(url="www.tavily.com", max_depth=2, instructions="Find all pages")` | Map website structure and discover URLs starting from a base URL without content extraction |
+| exa_search | `agent.tool.exa_search(query="Best project management tools", text=True)` | Intelligent web search with auto mode (default) that combines neural and keyword search for optimal results |
+| exa_get_contents | `agent.tool.exa_get_contents(urls=["https://example.com/article"], text=True, summary={"query": "key points"})` | Extract full content and summaries from specific URLs with live crawling fallback |
+| python_repl* | `agent.tool.python_repl(code="import pandas as pd\ndf = pd.read_csv('data.csv')\nprint(df.head())")` | Running Python code snippets, data analysis, executing complex logic with user confirmation for security |
| calculator | `agent.tool.calculator(expression="2 * sin(pi/4) + log(e**2)")` | Performing mathematical operations, symbolic math, equation solving |
-| code_interpreter | `code_interpreter = AgentCoreCodeInterpreter(region="us-west-2"); agent = Agent(tools=[code_interpreter.code_interpreter])` | Execute code in isolated sandbox environments with multi-language support (Python, JavaScript, TypeScript), persistent sessions, and file operations |
-| use_aws | `agent.tool.use_aws(service_name="s3", operation_name="list_buckets", parameters={}, region="us-west-2")` | Interacting with AWS services, cloud resource management |
-| retrieve | `agent.tool.retrieve(text="What is STRANDS?")` | Retrieving information from Amazon Bedrock Knowledge Bases with optional metadata |
-| nova_reels | `agent.tool.nova_reels(action="create", text="A cinematic shot of mountains", s3_bucket="my-bucket")` | Create high-quality videos using Amazon Bedrock Nova Reel with configurable parameters via environment variables |
-| agent_core_memory | `agent.tool.agent_core_memory(action="record", content="Hello, I like vegetarian food")` | Store and retrieve memories with Amazon Bedrock Agent Core Memory service |
-| mem0_memory | `agent.tool.mem0_memory(action="store", content="Remember I like to play tennis", user_id="alex")` | Store user and agent memories across agent runs to provide personalized experience |
-| bright_data | `agent.tool.bright_data(action="scrape_as_markdown", url="https://example.com")` | Web scraping, search queries, screenshot capture, and structured data extraction from websites and different data feeds|
-| memory | `agent.tool.memory(action="retrieve", query="product features")` | Store, retrieve, list, and manage documents in Amazon Bedrock Knowledge Bases with configurable parameters via environment variables |
-| environment | `agent.tool.environment(action="list", prefix="AWS_")` | Managing environment variables, configuration management |
-| generate_image_stability | `agent.tool.generate_image_stability(prompt="A tranquil pool")` | Creating images using Stability AI models |
-| generate_image | `agent.tool.generate_image(prompt="A sunset over mountains")` | Creating AI-generated images for various applications |
-| image_reader | `agent.tool.image_reader(image_path="path/to/image.jpg")` | Processing and reading image files for AI analysis |
-| journal | `agent.tool.journal(action="write", content="Today's progress notes")` | Creating structured logs, maintaining documentation |
-| think | `agent.tool.think(thought="Complex problem to analyze", cycle_count=3)` | Advanced reasoning, multi-step thinking processes |
-| load_tool | `agent.tool.load_tool(path="path/to/custom_tool.py", name="custom_tool")` | Dynamically loading custom tools and extensions |
-| swarm | `agent.tool.swarm(task="Analyze this problem", swarm_size=3, coordination_pattern="collaborative")` | Coordinating multiple AI agents to solve complex problems through collective intelligence |
-| current_time | `agent.tool.current_time(timezone="US/Pacific")` | Get the current time in ISO 8601 format for a specified timezone |
-| sleep | `agent.tool.sleep(seconds=5)` | Pause execution for the specified number of seconds, interruptible with SIGINT (Ctrl+C) |
-| agent_graph | `agent.tool.agent_graph(agents=["agent1", "agent2"], connections=[{"from": "agent1", "to": "agent2"}])` | Create and visualize agent relationship graphs for complex multi-agent systems |
-| cron* | `agent.tool.cron(action="schedule", name="task", schedule="0 * * * *", command="backup.sh")` | Schedule and manage recurring tasks with cron job syntax
**Does not work on Windows |
-| slack | `agent.tool.slack(action="post_message", channel="general", text="Hello team!")` | Interact with Slack workspace for messaging and monitoring |
-| speak | `agent.tool.speak(text="Operation completed successfully", style="green", mode="polly")` | Output status messages with rich formatting and optional text-to-speech |
-| stop | `agent.tool.stop(message="Process terminated by user request")` | Gracefully terminate agent execution with custom message |
-| handoff_to_user | `agent.tool.handoff_to_user(message="Please confirm action", breakout_of_loop=False)` | Hand off control to user for confirmation, input, or complete task handoff |
-| use_llm | `agent.tool.use_llm(prompt="Analyze this data", system_prompt="You are a data analyst")` | Create nested AI loops with customized system prompts for specialized tasks |
-| workflow | `agent.tool.workflow(action="create", name="data_pipeline", steps=[{"tool": "file_read"}, {"tool": "python_repl"}])` | Define, execute, and manage multi-step automated workflows |
-| mcp_client | `agent.tool.mcp_client(action="connect", connection_id="my_server", transport="stdio", command="python", args=["server.py"])` | ⚠️ **SECURITY WARNING**: Dynamically connect to external MCP servers via stdio, sse, or streamable_http, list tools, and call remote tools. This can pose security risks as agents may connect to malicious servers. Use with caution in production. |
-| batch| `agent.tool.batch(invocations=[{"name": "current_time", "arguments": {"timezone": "Europe/London"}}, {"name": "stop", "arguments": {}}])` | Call multiple other tools in parallel. |
-| browser | `browser = LocalChromiumBrowser(); agent = Agent(tools=[browser.browser])` | Web scraping, automated testing, form filling, web automation tasks |
-| diagram | `agent.tool.diagram(diagram_type="cloud", nodes=[{"id": "s3", "type": "S3"}], edges=[])` | Create AWS cloud architecture diagrams, network diagrams, graphs, and UML diagrams (all 14 types) |
-| rss | `agent.tool.rss(action="subscribe", url="https://example.com/feed.xml", feed_id="tech_news")` | Manage RSS feeds: subscribe, fetch, read, search, and update content from various sources |
-| use_computer | `agent.tool.use_computer(action="click", x=100, y=200, app_name="Chrome") ` | Desktop automation, GUI interaction, screen capture |
-| search_video | `agent.tool.search_video(query="people discussing AI")` | Semantic video search using TwelveLabs' Marengo model |
-| chat_video | `agent.tool.chat_video(prompt="What are the main topics?", video_id="video_123")` | Interactive video analysis using TwelveLabs' Pegasus model |
-| mongodb_memory | `agent.tool.mongodb_memory(action="record", content="User prefers vegetarian pizza", connection_string="mongodb+srv://...", database_name="memories")` | Store and retrieve memories using MongoDB Atlas with semantic search via AWS Bedrock Titan embeddings |
-
-\* *These tools do not work on windows*
-
+| json_processor | `agent.tool.json_processor(action="validate", data='{"name": "John"}')` | JSON validation, formatting, querying, and manipulation with dot notation support |
+| code_interpreter | `code_interpreter = AgentCoreCodeInterpreter(region="us-west-2"); agent = Agent(tools=[code_interpreter.code_interpreter])` | Execute code in isolated sandbox environments with multi-language support (Python, JavaScript, TypeScript), persistent sessions, and file operations |
+| use_aws | `agent.tool.use_aws(service_name="s3", operation_name="list_buckets", parameters={}, region="us-west-2")` | Interacting with AWS services, cloud resource management |
+| retrieve | `agent.tool.retrieve(text="What is STRANDS?")` | Retrieving information from Amazon Bedrock Knowledge Bases with optional metadata |
+| nova_reels | `agent.tool.nova_reels(action="create", text="A cinematic shot of mountains", s3_bucket="my-bucket")` | Create high-quality videos using Amazon Bedrock Nova Reel with configurable parameters via environment variables |
+| agent_core_memory | `agent.tool.agent_core_memory(action="record", content="Hello, I like vegetarian food")` | Store and retrieve memories with Amazon Bedrock Agent Core Memory service |
+| mem0_memory | `agent.tool.mem0_memory(action="store", content="Remember I like to play tennis", user_id="alex")` | Store user and agent memories across agent runs to provide personalized experience |
+| bright_data | `agent.tool.bright_data(action="scrape_as_markdown", url="https://example.com")` | Web scraping, search queries, screenshot capture, and structured data extraction from websites and different data feeds|
+| memory | `agent.tool.memory(action="retrieve", query="product features")` | Store, retrieve, list, and manage documents in Amazon Bedrock Knowledge Bases with configurable parameters via environment variables |
+| environment | `agent.tool.environment(action="list", prefix="AWS_")` | Managing environment variables, configuration management |
+| generate_image_stability | `agent.tool.generate_image_stability(prompt="A tranquil pool")` | Creating images using Stability AI models |
+| generate_image | `agent.tool.generate_image(prompt="A sunset over mountains")` | Creating AI-generated images for various applications |
+| image_reader | `agent.tool.image_reader(image_path="path/to/image.jpg")` | Processing and reading image files for AI analysis |
+| journal | `agent.tool.journal(action="write", content="Today's progress notes")` | Creating structured logs, maintaining documentation |
+| think | `agent.tool.think(thought="Complex problem to analyze", cycle_count=3)` | Advanced reasoning, multi-step thinking processes |
+| load_tool | `agent.tool.load_tool(path="path/to/custom_tool.py", name="custom_tool")` | Dynamically loading custom tools and extensions |
+| swarm | `agent.tool.swarm(task="Analyze this problem", swarm_size=3, coordination_pattern="collaborative")` | Coordinating multiple AI agents to solve complex problems through collective intelligence |
+| current_time | `agent.tool.current_time(timezone="US/Pacific")` | Get the current time in ISO 8601 format for a specified timezone |
+| sleep | `agent.tool.sleep(seconds=5)` | Pause execution for the specified number of seconds, interruptible with SIGINT (Ctrl+C) |
+| agent_graph | `agent.tool.agent_graph(agents=["agent1", "agent2"], connections=[{"from": "agent1", "to": "agent2"}])` | Create and visualize agent relationship graphs for complex multi-agent systems |
+| cron* | `agent.tool.cron(action="schedule", name="task", schedule="0 * * * *", command="backup.sh")` | Schedule and manage recurring tasks with cron job syntax
**Does not work on Windows |
+| slack | `agent.tool.slack(action="post_message", channel="general", text="Hello team!")` | Interact with Slack workspace for messaging and monitoring |
+| speak | `agent.tool.speak(text="Operation completed successfully", style="green", mode="polly")` | Output status messages with rich formatting and optional text-to-speech |
+| stop | `agent.tool.stop(message="Process terminated by user request")` | Gracefully terminate agent execution with custom message |
+| handoff_to_user | `agent.tool.handoff_to_user(message="Please confirm action", breakout_of_loop=False)` | Hand off control to user for confirmation, input, or complete task handoff |
+| use_llm | `agent.tool.use_llm(prompt="Analyze this data", system_prompt="You are a data analyst")` | Create nested AI loops with customized system prompts for specialized tasks |
+| workflow | `agent.tool.workflow(action="create", name="data_pipeline", steps=[{"tool": "file_read"}, {"tool": "python_repl"}])` | Define, execute, and manage multi-step automated workflows |
+| mcp_client | `agent.tool.mcp_client(action="connect", connection_id="my_server", transport="stdio", command="python", args=["server.py"])` | ⚠️ **SECURITY WARNING**: Dynamically connect to external MCP servers via stdio, sse, or streamable_http, list tools, and call remote tools. This can pose security risks as agents may connect to malicious servers. Use with caution in production. |
+| batch| `agent.tool.batch(invocations=[{"name": "current_time", "arguments": {"timezone": "Europe/London"}}, {"name": "stop", "arguments": {}}])` | Call multiple other tools in parallel. |
+| browser | `browser = LocalChromiumBrowser(); agent = Agent(tools=[browser.browser])` | Web scraping, automated testing, form filling, web automation tasks |
+| diagram | `agent.tool.diagram(diagram_type="cloud", nodes=[{"id": "s3", "type": "S3"}], edges=[])` | Create AWS cloud architecture diagrams, network diagrams, graphs, and UML diagrams (all 14 types) |
+| rss | `agent.tool.rss(action="subscribe", url="https://example.com/feed.xml", feed_id="tech_news")` | Manage RSS feeds: subscribe, fetch, read, search, and update content from various sources |
+| use_computer | `agent.tool.use_computer(action="click", x=100, y=200, app_name="Chrome") ` | Desktop automation, GUI interaction, screen capture |
+| search_video | `agent.tool.search_video(query="people discussing AI")` | Semantic video search using TwelveLabs' Marengo model |
+| chat_video | `agent.tool.chat_video(prompt="What are the main topics?", video_id="video_123")` | Interactive video analysis using TwelveLabs' Pegasus model |
+| mongodb_memory | `agent.tool.mongodb_memory(action="record", content="User prefers vegetarian pizza", connection_string="mongodb+srv://...", database_name="memories")` | Store and retrieve memories using MongoDB Atlas with semantic search via AWS Bedrock Titan embeddings |
+
+\* *These tools do not work on windows*
+
+## 💻 Usage Examples
+
## 💻 Usage Examples
-### File Operations
-
-```python
-from strands import Agent
-from strands_tools import file_read, file_write, editor
-
-agent = Agent(tools=[file_read, file_write, editor])
-
-agent.tool.file_read(path="config.json")
-agent.tool.file_write(path="output.txt", content="Hello, world!")
-agent.tool.editor(command="view", path="script.py")
-```
-
-### Dynamic MCP Client Integration
-
-⚠️ **SECURITY WARNING**: The Dynamic MCP Client allows agents to autonomously connect to external MCP servers and load remote tools at runtime. This poses significant security risks as agents can potentially connect to malicious servers and execute untrusted code. Use with extreme caution in production environments.
-
-This tool is different from the static MCP server implementation in the Strands SDK (see [MCP Tools Documentation](https://github.com/strands-agents/docs/blob/main/docs/user-guide/concepts/tools/mcp-tools.md)) which uses pre-configured, trusted MCP servers.
-
-```python
-from strands import Agent
-from strands_tools import mcp_client
-
-agent = Agent(tools=[mcp_client])
-
-# Connect to a custom MCP server via stdio
-agent.tool.mcp_client(
- action="connect",
- connection_id="my_tools",
- transport="stdio",
- command="python",
- args=["my_mcp_server.py"]
-)
-
-# List available tools on the server
-tools = agent.tool.mcp_client(
- action="list_tools",
- connection_id="my_tools"
-)
-
-# Call a tool from the MCP server
-result = agent.tool.mcp_client(
- action="call_tool",
- connection_id="my_tools",
- tool_name="calculate",
- tool_args={"x": 10, "y": 20}
-)
-
-# Connect to a SSE-based server
-agent.tool.mcp_client(
- action="connect",
- connection_id="web_server",
- transport="sse",
- server_url="http://localhost:8080/sse"
-)
-
-# Connect to a streamable HTTP server
-agent.tool.mcp_client(
- action="connect",
- connection_id="http_server",
- transport="streamable_http",
- server_url="https://api.example.com/mcp",
- headers={"Authorization": "Bearer token"},
- timeout=60
-)
-
-# Load MCP tools into agent's registry for direct access
-# ⚠️ WARNING: This loads external tools directly into the agent
-agent.tool.mcp_client(
- action="load_tools",
- connection_id="my_tools"
-)
-# Now you can call MCP tools directly as: agent.tool.calculate(x=10, y=20)
-```
-
-### Shell Commands
-
-*Note: `shell` does not work on Windows.*
-
-```python
-from strands import Agent
-from strands_tools import shell
-
-agent = Agent(tools=[shell])
-
-# Execute a single command
-result = agent.tool.shell(command="ls -la")
-
-# Execute a sequence of commands
-results = agent.tool.shell(command=["mkdir -p test_dir", "cd test_dir", "touch test.txt"])
-
-# Execute commands with error handling
-agent.tool.shell(command="risky-command", ignore_errors=True)
-```
-
-### HTTP Requests
-
-```python
-from strands import Agent
-from strands_tools import http_request
-
-agent = Agent(tools=[http_request])
-
-# Make a simple GET request
-response = agent.tool.http_request(
- method="GET",
- url="https://api.example.com/data"
-)
-
-# POST request with authentication
-response = agent.tool.http_request(
- method="POST",
- url="https://api.example.com/resource",
- headers={"Content-Type": "application/json"},
- body=json.dumps({"key": "value"}),
- auth_type="Bearer",
- auth_token="your_token_here"
-)
-
-# Convert HTML webpages to markdown for better readability
-response = agent.tool.http_request(
- method="GET",
- url="https://example.com/article",
- convert_to_markdown=True
-)
-```
-
-### Tavily Search, Extract, Crawl, and Map
-
-```python
-from strands import Agent
-from strands_tools.tavily import (
- tavily_search, tavily_extract, tavily_crawl, tavily_map
-)
-
-# For async usage, call the corresponding *_async function with await.
-# Synchronous usage
-agent = Agent(tools=[tavily_search, tavily_extract, tavily_crawl, tavily_map])
-
-# Real-time web search
-result = agent.tool.tavily_search(
- query="Latest developments in renewable energy",
- search_depth="advanced",
- topic="news",
- max_results=10,
- include_raw_content=True
-)
-
-# Extract content from multiple URLs
-result = agent.tool.tavily_extract(
- urls=["www.tavily.com", "www.apple.com"],
- extract_depth="advanced",
- format="markdown"
-)
-
-# Advanced crawl with instructions and filtering
-result = agent.tool.tavily_crawl(
- url="www.tavily.com",
- max_depth=2,
- limit=50,
- instructions="Find all API documentation and developer guides",
- extract_depth="advanced",
- include_images=True
-)
-
-# Basic website mapping
-result = agent.tool.tavily_map(url="www.tavily.com")
-
-```
-
-### Exa Search and Contents
-
-```python
-from strands import Agent
-from strands_tools.exa import exa_search, exa_get_contents
-
-agent = Agent(tools=[exa_search, exa_get_contents])
-
-# Basic search (auto mode is default and recommended)
-result = agent.tool.exa_search(
- query="Best project management software",
- text=True
-)
-
-# Company-specific search when needed
-result = agent.tool.exa_search(
- query="Anthropic AI safety research",
- category="company",
- include_domains=["anthropic.com"],
- num_results=5,
- summary={"query": "key research areas and findings"}
-)
-
-# News search with date filtering
-result = agent.tool.exa_search(
- query="AI regulation policy updates",
- category="news",
- start_published_date="2024-01-01T00:00:00.000Z",
- text=True
-)
-
-# Get detailed content from specific URLs
-result = agent.tool.exa_get_contents(
- urls=[
- "https://example.com/blog-post",
- "https://github.com/microsoft/semantic-kernel"
- ],
- text={"maxCharacters": 5000, "includeHtmlTags": False},
- summary={
- "query": "main points and practical applications"
- },
- subpages=2,
- extras={"links": 5, "imageLinks": 2}
-)
-
-# Structured summary with JSON schema
-result = agent.tool.exa_get_contents(
- urls=["https://example.com/article"],
- summary={
- "query": "main findings and recommendations",
- "schema": {
- "type": "object",
- "properties": {
- "main_points": {"type": "string", "description": "Key points from the article"},
- "recommendations": {"type": "string", "description": "Suggested actions or advice"},
- "conclusion": {"type": "string", "description": "Overall conclusion"},
- "relevance": {"type": "string", "description": "Why this matters"}
- },
- "required": ["main_points", "conclusion"]
- }
- }
-)
-
-```
-
-### Python Code Execution
-
-*Note: `python_repl` does not work on Windows.*
-
-```python
-from strands import Agent
-from strands_tools import python_repl
-
-agent = Agent(tools=[python_repl])
-
-# Execute Python code with state persistence
-result = agent.tool.python_repl(code="""
-import pandas as pd
-
-# Load and process data
-data = pd.read_csv('data.csv')
-processed = data.groupby('category').mean()
-
-processed.head()
-""")
-```
-
-### Code Interpreter
-
-```python
-from strands import Agent
-from strands_tools.code_interpreter import AgentCoreCodeInterpreter
-
-# Create the code interpreter tool
-bedrock_agent_core_code_interpreter = AgentCoreCodeInterpreter(region="us-west-2")
-agent = Agent(tools=[bedrock_agent_core_code_interpreter.code_interpreter])
-
-# Create a session
-agent.tool.code_interpreter({
- "action": {
- "type": "initSession",
- "description": "Data analysis session",
- "session_name": "analysis-session"
- }
-})
-
-# Execute Python code
-agent.tool.code_interpreter({
- "action": {
- "type": "executeCode",
- "session_name": "analysis-session",
- "code": "print('Hello from sandbox!')",
- "language": "python"
- }
-})
-```
-
-### Swarm Intelligence
-
-```python
-from strands import Agent
-from strands_tools import swarm
-
-agent = Agent(tools=[swarm])
-
-# Create a collaborative swarm of agents to tackle a complex problem
-result = agent.tool.swarm(
- task="Generate creative solutions for reducing plastic waste in urban areas",
- swarm_size=5,
- coordination_pattern="collaborative"
-)
-
-# Create a competitive swarm for diverse solution generation
-result = agent.tool.swarm(
- task="Design an innovative product for smart home automation",
- swarm_size=3,
- coordination_pattern="competitive"
-)
-
-# Hybrid approach combining collaboration and competition
-result = agent.tool.swarm(
- task="Develop marketing strategies for a new sustainable fashion brand",
- swarm_size=4,
- coordination_pattern="hybrid"
-)
-```
-
-### Use AWS
-
-```python
-from strands import Agent
-from strands_tools import use_aws
-
-agent = Agent(tools=[use_aws])
-
-# List S3 buckets
-result = agent.tool.use_aws(
- service_name="s3",
- operation_name="list_buckets",
- parameters={},
- region="us-east-1",
- label="List all S3 buckets"
-)
-
-# Get the contents of a specific S3 bucket
-result = agent.tool.use_aws(
- service_name="s3",
- operation_name="list_objects_v2",
- parameters={"Bucket": "example-bucket"}, # Replace with your actual bucket name
- region="us-east-1",
- label="List objects in a specific S3 bucket"
-)
-
-# Get the list of EC2 subnets
-result = agent.tool.use_aws(
- service_name="ec2",
- operation_name="describe_subnets",
- parameters={},
- region="us-east-1",
- label="List all subnets"
-)
-```
-
-### Retrieve Tool
-
-```python
-from strands import Agent
-from strands_tools import retrieve
-
-agent = Agent(tools=[retrieve])
-
-# Basic retrieval without metadata
-result = agent.tool.retrieve(
- text="What is artificial intelligence?"
-)
-
-# Retrieval with metadata enabled
-result = agent.tool.retrieve(
- text="What are the latest developments in machine learning?",
- enableMetadata=True
-)
-
-# Using environment variable to set default metadata behavior
-# Set RETRIEVE_ENABLE_METADATA_DEFAULT=true in your environment
-result = agent.tool.retrieve(
- text="Tell me about cloud computing"
- # enableMetadata will default to the environment variable value
-)
-```
-
-### Batch Tool
-
-```python
-import os
-import sys
-
-from strands import Agent
-from strands_tools import batch, http_request, use_aws
-
-# Example usage of the batch with http_request and use_aws tools
-agent = Agent(tools=[batch, http_request, use_aws])
-
-result = agent.tool.batch(
- invocations=[
- {"name": "http_request", "arguments": {"method": "GET", "url": "https://api.ipify.org?format=json"}},
- {
- "name": "use_aws",
- "arguments": {
- "service_name": "s3",
- "operation_name": "list_buckets",
- "parameters": {},
- "region": "us-east-1",
- "label": "List S3 Buckets"
- }
- },
- ]
-)
-```
-
-### Video Tools
-
-```python
-from strands import Agent
-from strands_tools import search_video, chat_video
-
-agent = Agent(tools=[search_video, chat_video])
-
-# Search for video content using natural language
-result = agent.tool.search_video(
- query="people discussing AI technology",
- threshold="high",
- group_by="video",
- page_limit=5
-)
-
-# Chat with existing video (no index_id needed)
-result = agent.tool.chat_video(
- prompt="What are the main topics discussed in this video?",
- video_id="existing-video-id"
-)
-
-# Chat with new video file (index_id required for upload)
-result = agent.tool.chat_video(
- prompt="Describe what happens in this video",
- video_path="/path/to/video.mp4",
- index_id="your-index-id" # or set TWELVELABS_PEGASUS_INDEX_ID env var
-)
-```
+### JSON Processing
-### AgentCore Memory
```python
from strands import Agent
-from strands_tools.agent_core_memory import AgentCoreMemoryToolProvider
-
-
-provider = AgentCoreMemoryToolProvider(
- memory_id="memory-123abc", # Required
- actor_id="user-456", # Required
- session_id="session-789", # Required
- namespace="default", # Required
- region="us-west-2" # Optional, defaults to us-west-2
-)
+from strands_tools import json_processor
-agent = Agent(tools=provider.tools)
+agent = Agent(tools=[json_processor])
-# Create a new memory
-result = agent.tool.agent_core_memory(
- action="record",
- content="I am allergic to shellfish"
-)
-
-# Search for relevant memories
-result = agent.tool.agent_core_memory(
- action="retrieve",
- query="user preferences"
-)
+# Validate JSON data
+result = agent.tool.json_processor(action="validate", data='{"name": "John", "age": 30}')
-# List all memories
-result = agent.tool.agent_core_memory(
- action="list"
+# Format JSON with custom indentation
+result = agent.tool.json_processor(
+ action="format",
+ data={"name": "John", "age": 30, "city": "NYC"},
+ indent=4
)
-# Get a specific memory by ID
-result = agent.tool.agent_core_memory(
- action="get",
- memory_record_id="mr-12345"
-)
-```
-
-### Browser
-```python
-from strands import Agent
-from strands_tools.browser import LocalChromiumBrowser
-
-# Create browser tool
-browser = LocalChromiumBrowser()
-agent = Agent(tools=[browser.browser])
-
-# Simple navigation
-result = agent.tool.browser({
- "action": {
- "type": "navigate",
- "url": "https://example.com"
- }
-})
-
-# Initialize a session first
-result = agent.tool.browser({
- "action": {
- "type": "initSession",
- "session_name": "main-session",
- "description": "Web automation session"
- }
-})
-```
-
-### Handoff to User
-
-```python
-from strands import Agent
-from strands_tools import handoff_to_user
-
-agent = Agent(tools=[handoff_to_user])
+# Query nested data using dot notation
+data = {"user": {"profile": {"name": "John", "contacts": {"email": "john@example.com"}}}}
+result = agent.tool.json_processor(action="query", data=data, query_path="user.profile.name")
-# Request user confirmation and continue
-response = agent.tool.handoff_to_user(
- message="I need your approval to proceed with deleting these files. Type 'yes' to confirm.",
- breakout_of_loop=False
-)
-
-# Complete handoff to user (stops agent execution)
-agent.tool.handoff_to_user(
- message="Task completed. Please review the results and take any necessary follow-up actions.",
- breakout_of_loop=True
-)
-```
-
-### A2A Client
-
-```python
-from strands import Agent
-from strands_tools.a2a_client import A2AClientToolProvider
-
-# Initialize the A2A client provider with known agent URLs
-provider = A2AClientToolProvider(known_agent_urls=["http://localhost:9000"])
-agent = Agent(tools=provider.tools)
-
-# Use natural language to interact with A2A agents
-response = agent("discover available agents and send a greeting message")
-
-# The agent will automatically use the available tools:
-# - discover_agent(url) to find agents
-# - list_discovered_agents() to see all discovered agents
-# - send_message(message_text, target_agent_url) to communicate
-```
-
-### Diagram
-
-```python
-from strands import Agent
-from strands_tools import diagram
-
-agent = Agent(tools=[diagram])
-
-# Create an AWS cloud architecture diagram
-result = agent.tool.diagram(
- diagram_type="cloud",
- nodes=[
- {"id": "users", "type": "Users", "label": "End Users"},
- {"id": "cloudfront", "type": "CloudFront", "label": "CDN"},
- {"id": "s3", "type": "S3", "label": "Static Assets"},
- {"id": "api", "type": "APIGateway", "label": "API Gateway"},
- {"id": "lambda", "type": "Lambda", "label": "Backend Service"}
- ],
- edges=[
- {"from": "users", "to": "cloudfront"},
- {"from": "cloudfront", "to": "s3"},
- {"from": "users", "to": "api"},
- {"from": "api", "to": "lambda"}
- ],
- title="Web Application Architecture"
-)
-
-# Create a UML class diagram
-result = agent.tool.diagram(
- diagram_type="class",
- elements=[
- {
- "name": "User",
- "attributes": ["+id: int", "-name: string", "#email: string"],
- "methods": ["+login(): bool", "+logout(): void"]
- },
- {
- "name": "Order",
- "attributes": ["+id: int", "-items: List", "-total: float"],
- "methods": ["+addItem(item): void", "+calculateTotal(): float"]
- }
- ],
- relationships=[
- {"from": "User", "to": "Order", "type": "association", "multiplicity": "1..*"}
- ],
- title="E-commerce Domain Model"
-)
-```
+# Get all keys from JSON object
+result = agent.tool.json_processor(action="keys", data={"name": "John", "age": 30})
-### RSS Feed Management
+# Minify JSON for compact representation
+result = agent.tool.json_processor(action="minify", data={"name": "John", "age": 30})
-```python
-from strands import Agent
-from strands_tools import rss
-
-agent = Agent(tools=[rss])
-
-# Subscribe to a feed
-result = agent.tool.rss(
- action="subscribe",
- url="https://news.example.com/rss/technology"
-)
-
-# List all subscribed feeds
-feeds = agent.tool.rss(action="list")
-
-# Read entries from a specific feed
-entries = agent.tool.rss(
- action="read",
- feed_id="news_example_com_technology",
- max_entries=5,
- include_content=True
-)
-
-# Search across all feeds
-search_results = agent.tool.rss(
- action="search",
- query="machine learning",
- max_entries=10
-)
-
-# Fetch feed content without subscribing
-latest_news = agent.tool.rss(
- action="fetch",
- url="https://blog.example.org/feed",
- max_entries=3
-)
-```
-
-### Use Computer
-
-```python
-from strands import Agent
-from strands_tools import use_computer
-
-agent = Agent(tools=[use_computer])
-
-# Find mouse position
-result = agent.tool.use_computer(action="mouse_position")
-
-# Automate adding text
-result = agent.tool.use_computer(action="type", text="Hello, world!", app_name="Notepad")
-
-# Analyze current computer screen
-result = agent.tool.use_computer(action="analyze_screen")
-
-result = agent.tool.use_computer(action="open_app", app_name="Calculator")
-result = agent.tool.use_computer(action="close_app", app_name="Calendar")
-
-result = agent.tool.use_computer(
- action="hotkey",
- hotkey_str="command+ctrl+f", # For macOS
- app_name="Chrome"
-)
-```
-
-### Elasticsearch Memory
-
-**Note**: This tool requires AWS account credentials to generate embeddings using Amazon Bedrock Titan models.
-
-```python
-from strands import Agent
-from strands_tools.elasticsearch_memory import elasticsearch_memory
-
-# Create agent with direct tool usage
-agent = Agent(tools=[elasticsearch_memory])
-
-# Store a memory with semantic embeddings
-result = agent.tool.elasticsearch_memory(
- action="record",
- content="User prefers vegetarian pizza with extra cheese",
- metadata={"category": "food_preferences", "type": "dietary"},
- cloud_id="your-elasticsearch-cloud-id",
- api_key="your-api-key",
- index_name="memories",
- namespace="user_123"
-)
-
-# Search memories using semantic similarity (vector search)
-result = agent.tool.elasticsearch_memory(
- action="retrieve",
- query="food preferences and dietary restrictions",
- max_results=5,
- cloud_id="your-elasticsearch-cloud-id",
- api_key="your-api-key",
- index_name="memories",
- namespace="user_123"
-)
-
-# Use configuration dictionary for cleaner code
-config = {
- "cloud_id": "your-elasticsearch-cloud-id",
- "api_key": "your-api-key",
- "index_name": "memories",
- "namespace": "user_123"
-}
-
-# List all memories with pagination
-result = agent.tool.elasticsearch_memory(
- action="list",
- max_results=10,
- **config
-)
-
-# Get specific memory by ID
-result = agent.tool.elasticsearch_memory(
- action="get",
- memory_id="mem_1234567890_abcd1234",
- **config
-)
-
-# Delete a memory
-result = agent.tool.elasticsearch_memory(
- action="delete",
- memory_id="mem_1234567890_abcd1234",
- **config
-)
-
-# Use Elasticsearch Serverless (URL-based connection)
-result = agent.tool.elasticsearch_memory(
- action="record",
- content="User prefers vegetarian pizza",
- es_url="https://your-serverless-cluster.es.region.aws.elastic.cloud:443",
- api_key="your-api-key",
- index_name="memories",
- namespace="user_123"
-)
+# Get size and structure information
+result = agent.tool.json_processor(action="size", data={"users": [{"name": "John"}, {"name": "Jane"}]})
```
-### MongoDB Atlas Memory
-
-**Note**: This tool requires AWS account credentials to generate embeddings using Amazon Bedrock Titan models.
-
-```python
-from strands import Agent
-from strands_tools.mongodb_memory import mongodb_memory
-
-# Create agent with direct tool usage
-agent = Agent(tools=[mongodb_memory])
-
-# Store a memory with semantic embeddings
-result = agent.tool.mongodb_memory(
- action="record",
- content="User prefers vegetarian pizza with extra cheese",
- metadata={"category": "food_preferences", "type": "dietary"},
- connection_string="mongodb+srv://username:password@cluster0.mongodb.net/?retryWrites=true&w=majority",
- database_name="memories",
- collection_name="user_memories",
- namespace="user_123"
-)
-
-# Search memories using semantic similarity (vector search)
-result = agent.tool.mongodb_memory(
- action="retrieve",
- query="food preferences and dietary restrictions",
- max_results=5,
- connection_string="mongodb+srv://username:password@cluster0.mongodb.net/?retryWrites=true&w=majority",
- database_name="memories",
- collection_name="user_memories",
- namespace="user_123"
-)
-
-# Use configuration dictionary for cleaner code
-config = {
- "connection_string": "mongodb+srv://username:password@cluster0.mongodb.net/?retryWrites=true&w=majority",
- "database_name": "memories",
- "collection_name": "user_memories",
- "namespace": "user_123"
-}
-
-# List all memories with pagination
-result = agent.tool.mongodb_memory(
- action="list",
- max_results=10,
- **config
-)
-
-# Get specific memory by ID
-result = agent.tool.mongodb_memory(
- action="get",
- memory_id="mem_1234567890_abcd1234",
- **config
-)
-
-# Delete a memory
-result = agent.tool.mongodb_memory(
- action="delete",
- memory_id="mem_1234567890_abcd1234",
- **config
-)
-
-# Use environment variables for connection
-# Set MONGODB_ATLAS_CLUSTER_URI in your environment
-result = agent.tool.mongodb_memory(
- action="record",
- content="User prefers vegetarian pizza",
- database_name="memories",
- collection_name="user_memories",
- namespace="user_123"
-)
-```
-
-## 🌍 Environment Variables Configuration
-
-Agents Tools provides extensive customization through environment variables. This allows you to configure tool behavior without modifying code, making it ideal for different environments (development, testing, production).
-
-### Global Environment Variables
-
-These variables affect multiple tools:
-
-| Environment Variable | Description | Default | Affected Tools |
-|----------------------|-------------|---------|---------------|
-| BYPASS_TOOL_CONSENT | Bypass consent for tool invocation, set to "true" to enable | false | All tools that require consent (e.g. shell, file_write, python_repl) |
-| STRANDS_TOOL_CONSOLE_MODE | Enable rich UI for tools, set to "enabled" to enable | disabled | All tools that have optional rich UI |
-| AWS_REGION | Default AWS region for AWS operations | us-west-2 | use_aws, retrieve, generate_image, memory, nova_reels |
-| AWS_PROFILE | AWS profile name to use from ~/.aws/credentials | default | use_aws, retrieve |
-| LOG_LEVEL | Logging level (DEBUG, INFO, WARNING, ERROR) | INFO | All tools |
-
-### Tool-Specific Environment Variables
-
-#### Calculator Tool
-
-| Environment Variable | Description | Default |
-|----------------------|-------------|---------|
-| CALCULATOR_MODE | Default calculation mode | evaluate |
-| CALCULATOR_PRECISION | Number of decimal places for results | 10 |
-| CALCULATOR_SCIENTIFIC | Whether to use scientific notation for numbers | False |
-| CALCULATOR_FORCE_NUMERIC | Force numeric evaluation of symbolic expressions | False |
-| CALCULATOR_FORCE_SCIENTIFIC_THRESHOLD | Threshold for automatic scientific notation | 1e21 |
-| CALCULATOR_DERIVE_ORDER | Default order for derivatives | 1 |
-| CALCULATOR_SERIES_POINT | Default point for series expansion | 0 |
-| CALCULATOR_SERIES_ORDER | Default order for series expansion | 5 |
-
-#### Current Time Tool
-
-| Environment Variable | Description | Default |
-|----------------------|-------------|---------|
-| DEFAULT_TIMEZONE | Default timezone for current_time tool | UTC |
-
-#### Sleep Tool
-
-| Environment Variable | Description | Default |
-|----------------------|-------------|---------|
-| MAX_SLEEP_SECONDS | Maximum allowed sleep duration in seconds | 300 |
-
-#### Tavily Search, Extract, Crawl, and Map Tools
-
-| Environment Variable | Description | Default |
-|----------------------|-------------|---------|
-| TAVILY_API_KEY | Tavily API key (required for all Tavily functionality) | None |
-- Visit https://www.tavily.com/ to create a free account and API key.
-
-#### Exa Search and Contents Tools
-
-| Environment Variable | Description | Default |
-|----------------------|-------------|---------|
-| EXA_API_KEY | Exa API key (required for all Exa functionality) | None |
-- Visit https://dashboard.exa.ai/api-keys to create a free account and API key.
-
-#### Mem0 Memory Tool
-
-The Mem0 Memory Tool supports three different backend configurations:
-
-1. **Mem0 Platform**:
- - Uses the Mem0 Platform API for memory management
- - Requires a Mem0 API key
-
-2. **OpenSearch** (Recommended for AWS environments):
- - Uses OpenSearch as the vector store backend
- - Requires AWS credentials and OpenSearch configuration
-
-3. **FAISS** (Default for local development):
- - Uses FAISS as the local vector store backend
- - Requires faiss-cpu package for local vector storage
-
-4. **Neptune Analytics** (Optional Graph backend for search enhancement):
- - Uses Neptune Analytics as the graph store backend to enhance memory recall.
- - Requires AWS credentials and Neptune Analytics configuration
- ```
- # Configure your Neptune Analytics graph ID in the .env file:
- export NEPTUNE_ANALYTICS_GRAPH_IDENTIFIER=sample-graph-id
-
- # Configure your Neptune Analytics graph ID in Python code:
- import os
- os.environ['NEPTUNE_ANALYTICS_GRAPH_IDENTIFIER'] = "g-sample-graph-id"
-
- ```
-
-| Environment Variable | Description | Default | Required For |
-|----------------------|-------------|---------|--------------|
-| MEM0_API_KEY | Mem0 Platform API key | None | Mem0 Platform |
-| OPENSEARCH_HOST | OpenSearch Host URL | None | OpenSearch |
-| AWS_REGION | AWS Region for OpenSearch | us-west-2 | OpenSearch |
-| NEPTUNE_ANALYTICS_GRAPH_IDENTIFIER | Neptune Analytics Graph Identifier | None | Neptune Analytics |
-| DEV | Enable development mode (bypasses confirmations) | false | All modes |
-| MEM0_LLM_PROVIDER | LLM provider for memory processing | aws_bedrock | All modes |
-| MEM0_LLM_MODEL | LLM model for memory processing | anthropic.claude-3-5-haiku-20241022-v1:0 | All modes |
-| MEM0_LLM_TEMPERATURE | LLM temperature (0.0-2.0) | 0.1 | All modes |
-| MEM0_LLM_MAX_TOKENS | LLM maximum tokens | 2000 | All modes |
-| MEM0_EMBEDDER_PROVIDER | Embedder provider for vector embeddings | aws_bedrock | All modes |
-| MEM0_EMBEDDER_MODEL | Embedder model for vector embeddings | amazon.titan-embed-text-v2:0 | All modes |
-
-
-**Note**:
-- If `MEM0_API_KEY` is set, the tool will use the Mem0 Platform
-- If `OPENSEARCH_HOST` is set, the tool will use OpenSearch
-- If neither is set, the tool will default to FAISS (requires `faiss-cpu` package)
-- If `NEPTUNE_ANALYTICS_GRAPH_IDENTIFIER` is set, the tool will configure Neptune Analytics as graph store to enhance memory search
-- LLM configuration applies to all backend modes and allows customization of the language model used for memory processing
-
-#### Bright Data Tool
-
-| Environment Variable | Description | Default |
-|----------------------|-------------|---------|
-| BRIGHTDATA_API_KEY | Bright Data API Key | None |
-| BRIGHTDATA_ZONE | Bright Data Web Unlocker Zone | web_unlocker1 |
-
-#### Memory Tool
-
-| Environment Variable | Description | Default |
-|----------------------|-------------|---------|
-| MEMORY_DEFAULT_MAX_RESULTS | Default maximum results for list operations | 50 |
-| MEMORY_DEFAULT_MIN_SCORE | Default minimum relevance score for filtering results | 0.4 |
-
-#### Nova Reels Tool
-
-| Environment Variable | Description | Default |
-|----------------------|-------------|---------|
-| NOVA_REEL_DEFAULT_SEED | Default seed for video generation | 0 |
-| NOVA_REEL_DEFAULT_FPS | Default frames per second for generated videos | 24 |
-| NOVA_REEL_DEFAULT_DIMENSION | Default video resolution in WIDTHxHEIGHT format | 1280x720 |
-| NOVA_REEL_DEFAULT_MAX_RESULTS | Default maximum number of jobs to return for list action | 10 |
-
-#### Python REPL Tool
-
-| Environment Variable | Description | Default |
-|----------------------|-------------|---------|
-| PYTHON_REPL_BINARY_MAX_LEN | Maximum length for binary content before truncation | 100 |
-| PYTHON_REPL_INTERACTIVE | Whether to enable interactive PTY mode | None |
-| PYTHON_REPL_RESET_STATE | Whether to reset the REPL state before execution | None |
-| PYTHON_REPL_PERSISTENCE_DIR | Set Directory for python_repl tool to write state file | None |
-
-#### Shell Tool
-
-| Environment Variable | Description | Default |
-|----------------------|-------------|---------|
-| SHELL_DEFAULT_TIMEOUT | Default timeout in seconds for shell commands | 900 |
-
-#### Slack Tool
-
-| Environment Variable | Description | Default |
-|----------------------|-------------|---------|
-| SLACK_DEFAULT_EVENT_COUNT | Default number of events to retrieve | 42 |
-| STRANDS_SLACK_AUTO_REPLY | Enable automatic replies to messages | false |
-| STRANDS_SLACK_LISTEN_ONLY_TAG | Only process messages containing this tag | None |
-
-#### Speak Tool
-
-| Environment Variable | Description | Default |
-|----------------------|-------------|---------|
-| SPEAK_DEFAULT_STYLE | Default style for status messages | green |
-| SPEAK_DEFAULT_MODE | Default speech mode (fast/polly) | fast |
-| SPEAK_DEFAULT_VOICE_ID | Default Polly voice ID | Joanna |
-| SPEAK_DEFAULT_OUTPUT_PATH | Default audio output path | speech_output.mp3 |
-| SPEAK_DEFAULT_PLAY_AUDIO | Whether to play audio by default | True |
-
-#### Editor Tool
-
-| Environment Variable | Description | Default |
-|----------------------|-------------|---------|
-| EDITOR_DIR_TREE_MAX_DEPTH | Maximum depth for directory tree visualization | 2 |
-| EDITOR_DEFAULT_STYLE | Default style for output panels | default |
-| EDITOR_DEFAULT_LANGUAGE | Default language for syntax highlighting | python |
-| EDITOR_DISABLE_BACKUP | Skip creating .bak backup files during edit operations | false |
-
-#### Environment Tool
-
-| Environment Variable | Description | Default |
-|----------------------|-------------|---------|
-| ENV_VARS_MASKED_DEFAULT | Default setting for masking sensitive values | true |
-
-#### Dynamic MCP Client Tool
-
-| Environment Variable | Description | Default |
-|----------------------|-------------|---------|
-| STRANDS_MCP_TIMEOUT | Default timeout in seconds for MCP operations | 30.0 |
-
-#### File Read Tool
-
-| Environment Variable | Description | Default |
-|----------------------|-------------|---------|
-| FILE_READ_RECURSIVE_DEFAULT | Default setting for recursive file searching | true |
-| FILE_READ_CONTEXT_LINES_DEFAULT | Default number of context lines around search matches | 2 |
-| FILE_READ_START_LINE_DEFAULT | Default starting line number for lines mode | 0 |
-| FILE_READ_CHUNK_OFFSET_DEFAULT | Default byte offset for chunk mode | 0 |
-| FILE_READ_DIFF_TYPE_DEFAULT | Default diff type for file comparisons | unified |
-| FILE_READ_USE_GIT_DEFAULT | Default setting for using git in time machine mode | true |
-| FILE_READ_NUM_REVISIONS_DEFAULT | Default number of revisions to show in time machine mode | 5 |
-
-#### Browser Tool
-
-| Environment Variable | Description | Default |
-|----------------------|-------------|---------|
-| STRANDS_DEFAULT_WAIT_TIME | Default setting for wait time with actions | 1 |
-| STRANDS_BROWSER_MAX_RETRIES | Default number of retries to perform when an action fails | 3 |
-| STRANDS_BROWSER_RETRY_DELAY | Default retry delay time for retry mechanisms | 1 |
-| STRANDS_BROWSER_SCREENSHOTS_DIR | Default directory where screenshots will be saved | screenshots |
-| STRANDS_BROWSER_USER_DATA_DIR | Default directory where data for reloading a browser instance is stored | ~/.browser_automation |
-| STRANDS_BROWSER_HEADLESS | Default headless setting for launching browsers | false |
-| STRANDS_BROWSER_WIDTH | Default width of the browser | 1280 |
-| STRANDS_BROWSER_HEIGHT | Default height of the browser | 800 |
-
-#### RSS Tool
-
-| Environment Variable | Description | Default |
-|----------------------|-------------|---------|
-| STRANDS_RSS_MAX_ENTRIES | Default setting for maximum number of entries per feed | 100 |
-| STRANDS_RSS_UPDATE_INTERVAL | Default amount of time between updating rss feeds in minutes | 60 |
-| STRANDS_RSS_STORAGE_PATH | Default storage path where rss feeds are stored locally | strands_rss_feeds (this may vary based on your system) |
-
-#### Retrieve Tool
-
-| Environment Variable | Description | Default |
-|----------------------|-------------|---------|
-| RETRIEVE_ENABLE_METADATA_DEFAULT | Default setting for enabling metadata in retrieve tool responses | false |
-
-#### Video Tools
-
-| Environment Variable | Description | Default |
-|----------------------|-------------|---------|
-| TWELVELABS_API_KEY | TwelveLabs API key for video analysis | None |
-| TWELVELABS_MARENGO_INDEX_ID | Default index ID for search_video tool | None |
-| TWELVELABS_PEGASUS_INDEX_ID | Default index ID for chat_video tool | None |
-
-#### MongoDB Atlas Memory Tool
-
-| Environment Variable | Description | Default |
-|----------------------|-------------|---------|
-| MONGODB_ATLAS_CLUSTER_URI | MongoDB Atlas connection string | None |
-| MONGODB_DEFAULT_DATABASE | Default database name for MongoDB operations | memories |
-| MONGODB_DEFAULT_COLLECTION | Default collection name for MongoDB operations | user_memories |
-| MONGODB_DEFAULT_NAMESPACE | Default namespace for memory isolation | default |
-| MONGODB_DEFAULT_MAX_RESULTS | Default maximum results for list operations | 50 |
-| MONGODB_DEFAULT_MIN_SCORE | Default minimum relevance score for filtering results | 0.4 |
-
-**Note**: This tool requires AWS account credentials to generate embeddings using Amazon Bedrock Titan models.
-
-
-## Contributing ❤️
-
-This is a community-driven project, powered by passionate developers like you.
-We enthusiastically welcome contributions from everyone,
-regardless of experience level—your unique perspective is valuable to us!
-
-### How to Get Started?
-
-1. **Find your first opportunity**: If you're new to the project, explore our labeled "good first issues" for beginner-friendly tasks.
-2. **Understand our workflow**: Review our [Contributing Guide](CONTRIBUTING.md) to learn about our development setup, coding standards, and pull request process.
-3. **Make your impact**: Contributions come in many forms—fixing bugs, enhancing documentation, improving performance, adding features, writing tests, or refining the user experience.
-4. **Submit your work**: When you're ready, submit a well-documented pull request, and our maintainers will provide feedback to help get your changes merged.
-
-Your questions, insights, and ideas are always welcome!
-
-Together, we're building something meaningful that impacts real users. We look forward to collaborating with you!
-
-## License
-
-This project is licensed under the Apache License 2.0 - see the [LICENSE](LICENSE) file for details.
-
-## Security
-
-See [CONTRIBUTING](CONTRIBUTING.md#security-issue-notifications) for more information.
+### File Operations
+
+```python
+from strands import Agent
+from strands_tools import file_read, file_write, editor
+
+agent = Agent(tools=[file_read, file_write, editor])
+
+agent.tool.file_read(path="config.json")
+agent.tool.file_write(path="output.txt", content="Hello, world!")
+agent.tool.editor(command="view", path="script.py")
+```
+
+### Dynamic MCP Client Integration
+
+⚠️ **SECURITY WARNING**: The Dynamic MCP Client allows agents to autonomously connect to external MCP servers and load remote tools at runtime. This poses significant security risks as agents can potentially connect to malicious servers and execute untrusted code. Use with extreme caution in production environments.
+
+This tool is different from the static MCP server implementation in the Strands SDK (see [MCP Tools Documentation](https://github.com/strands-agents/docs/blob/main/docs/user-guide/concepts/tools/mcp-tools.md)) which uses pre-configured, trusted MCP servers.
+
+```python
+from strands import Agent
+from strands_tools import mcp_client
+
+agent = Agent(tools=[mcp_client])
+
+# Connect to a custom MCP server via stdio
+agent.tool.mcp_client(
+ action="connect",
+ connection_id="my_tools",
+ transport="stdio",
+ command="python",
+ args=["my_mcp_server.py"]
+)
+
+# List available tools on the server
+tools = agent.tool.mcp_client(
+ action="list_tools",
+ connection_id="my_tools"
+)
+
+# Call a tool from the MCP server
+result = agent.tool.mcp_client(
+ action="call_tool",
+ connection_id="my_tools",
+ tool_name="calculate",
+ tool_args={"x": 10, "y": 20}
+)
+
+# Connect to a SSE-based server
+agent.tool.mcp_client(
+ action="connect",
+ connection_id="web_server",
+ transport="sse",
+ server_url="http://localhost:8080/sse"
+)
+
+# Connect to a streamable HTTP server
+agent.tool.mcp_client(
+ action="connect",
+ connection_id="http_server",
+ transport="streamable_http",
+ server_url="https://api.example.com/mcp",
+ headers={"Authorization": "Bearer token"},
+ timeout=60
+)
+
+# Load MCP tools into agent's registry for direct access
+# ⚠️ WARNING: This loads external tools directly into the agent
+agent.tool.mcp_client(
+ action="load_tools",
+ connection_id="my_tools"
+)
+# Now you can call MCP tools directly as: agent.tool.calculate(x=10, y=20)
+```
+
+### Shell Commands
+
+*Note: `shell` does not work on Windows.*
+
+```python
+from strands import Agent
+from strands_tools import shell
+
+agent = Agent(tools=[shell])
+
+# Execute a single command
+result = agent.tool.shell(command="ls -la")
+
+# Execute a sequence of commands
+results = agent.tool.shell(command=["mkdir -p test_dir", "cd test_dir", "touch test.txt"])
+
+# Execute commands with error handling
+agent.tool.shell(command="risky-command", ignore_errors=True)
+```
+
+### HTTP Requests
+
+```python
+from strands import Agent
+from strands_tools import http_request
+
+agent = Agent(tools=[http_request])
+
+# Make a simple GET request
+response = agent.tool.http_request(
+ method="GET",
+ url="https://api.example.com/data"
+)
+
+# POST request with authentication
+response = agent.tool.http_request(
+ method="POST",
+ url="https://api.example.com/resource",
+ headers={"Content-Type": "application/json"},
+ body=json.dumps({"key": "value"}),
+ auth_type="Bearer",
+ auth_token="your_token_here"
+)
+
+# Convert HTML webpages to markdown for better readability
+response = agent.tool.http_request(
+ method="GET",
+ url="https://example.com/article",
+ convert_to_markdown=True
+)
+```
+
+### Tavily Search, Extract, Crawl, and Map
+
+```python
+from strands import Agent
+from strands_tools.tavily import (
+ tavily_search, tavily_extract, tavily_crawl, tavily_map
+)
+
+# For async usage, call the corresponding *_async function with await.
+# Synchronous usage
+agent = Agent(tools=[tavily_search, tavily_extract, tavily_crawl, tavily_map])
+
+# Real-time web search
+result = agent.tool.tavily_search(
+ query="Latest developments in renewable energy",
+ search_depth="advanced",
+ topic="news",
+ max_results=10,
+ include_raw_content=True
+)
+
+# Extract content from multiple URLs
+result = agent.tool.tavily_extract(
+ urls=["www.tavily.com", "www.apple.com"],
+ extract_depth="advanced",
+ format="markdown"
+)
+
+# Advanced crawl with instructions and filtering
+result = agent.tool.tavily_crawl(
+ url="www.tavily.com",
+ max_depth=2,
+ limit=50,
+ instructions="Find all API documentation and developer guides",
+ extract_depth="advanced",
+ include_images=True
+)
+
+# Basic website mapping
+result = agent.tool.tavily_map(url="www.tavily.com")
+
+```
+
+### Exa Search and Contents
+
+```python
+from strands import Agent
+from strands_tools.exa import exa_search, exa_get_contents
+
+agent = Agent(tools=[exa_search, exa_get_contents])
+
+# Basic search (auto mode is default and recommended)
+result = agent.tool.exa_search(
+ query="Best project management software",
+ text=True
+)
+
+# Company-specific search when needed
+result = agent.tool.exa_search(
+ query="Anthropic AI safety research",
+ category="company",
+ include_domains=["anthropic.com"],
+ num_results=5,
+ summary={"query": "key research areas and findings"}
+)
+
+# News search with date filtering
+result = agent.tool.exa_search(
+ query="AI regulation policy updates",
+ category="news",
+ start_published_date="2024-01-01T00:00:00.000Z",
+ text=True
+)
+
+# Get detailed content from specific URLs
+result = agent.tool.exa_get_contents(
+ urls=[
+ "https://example.com/blog-post",
+ "https://github.com/microsoft/semantic-kernel"
+ ],
+ text={"maxCharacters": 5000, "includeHtmlTags": False},
+ summary={
+ "query": "main points and practical applications"
+ },
+ subpages=2,
+ extras={"links": 5, "imageLinks": 2}
+)
+
+# Structured summary with JSON schema
+result = agent.tool.exa_get_contents(
+ urls=["https://example.com/article"],
+ summary={
+ "query": "main findings and recommendations",
+ "schema": {
+ "type": "object",
+ "properties": {
+ "main_points": {"type": "string", "description": "Key points from the article"},
+ "recommendations": {"type": "string", "description": "Suggested actions or advice"},
+ "conclusion": {"type": "string", "description": "Overall conclusion"},
+ "relevance": {"type": "string", "description": "Why this matters"}
+ },
+ "required": ["main_points", "conclusion"]
+ }
+ }
+)
+
+```
+
+### Python Code Execution
+
+*Note: `python_repl` does not work on Windows.*
+
+```python
+from strands import Agent
+from strands_tools import python_repl
+
+agent = Agent(tools=[python_repl])
+
+# Execute Python code with state persistence
+result = agent.tool.python_repl(code="""
+import pandas as pd
+
+# Load and process data
+data = pd.read_csv('data.csv')
+processed = data.groupby('category').mean()
+
+processed.head()
+""")
+```
+
+### Code Interpreter
+
+```python
+from strands import Agent
+from strands_tools.code_interpreter import AgentCoreCodeInterpreter
+
+# Create the code interpreter tool
+bedrock_agent_core_code_interpreter = AgentCoreCodeInterpreter(region="us-west-2")
+agent = Agent(tools=[bedrock_agent_core_code_interpreter.code_interpreter])
+
+# Create a session
+agent.tool.code_interpreter({
+ "action": {
+ "type": "initSession",
+ "description": "Data analysis session",
+ "session_name": "analysis-session"
+ }
+})
+
+# Execute Python code
+agent.tool.code_interpreter({
+ "action": {
+ "type": "executeCode",
+ "session_name": "analysis-session",
+ "code": "print('Hello from sandbox!')",
+ "language": "python"
+ }
+})
+```
+
+### Swarm Intelligence
+
+```python
+from strands import Agent
+from strands_tools import swarm
+
+agent = Agent(tools=[swarm])
+
+# Create a collaborative swarm of agents to tackle a complex problem
+result = agent.tool.swarm(
+ task="Generate creative solutions for reducing plastic waste in urban areas",
+ swarm_size=5,
+ coordination_pattern="collaborative"
+)
+
+# Create a competitive swarm for diverse solution generation
+result = agent.tool.swarm(
+ task="Design an innovative product for smart home automation",
+ swarm_size=3,
+ coordination_pattern="competitive"
+)
+
+# Hybrid approach combining collaboration and competition
+result = agent.tool.swarm(
+ task="Develop marketing strategies for a new sustainable fashion brand",
+ swarm_size=4,
+ coordination_pattern="hybrid"
+)
+```
+
+### Use AWS
+
+```python
+from strands import Agent
+from strands_tools import use_aws
+
+agent = Agent(tools=[use_aws])
+
+# List S3 buckets
+result = agent.tool.use_aws(
+ service_name="s3",
+ operation_name="list_buckets",
+ parameters={},
+ region="us-east-1",
+ label="List all S3 buckets"
+)
+
+# Get the contents of a specific S3 bucket
+result = agent.tool.use_aws(
+ service_name="s3",
+ operation_name="list_objects_v2",
+ parameters={"Bucket": "example-bucket"}, # Replace with your actual bucket name
+ region="us-east-1",
+ label="List objects in a specific S3 bucket"
+)
+
+# Get the list of EC2 subnets
+result = agent.tool.use_aws(
+ service_name="ec2",
+ operation_name="describe_subnets",
+ parameters={},
+ region="us-east-1",
+ label="List all subnets"
+)
+```
+
+### Retrieve Tool
+
+```python
+from strands import Agent
+from strands_tools import retrieve
+
+agent = Agent(tools=[retrieve])
+
+# Basic retrieval without metadata
+result = agent.tool.retrieve(
+ text="What is artificial intelligence?"
+)
+
+# Retrieval with metadata enabled
+result = agent.tool.retrieve(
+ text="What are the latest developments in machine learning?",
+ enableMetadata=True
+)
+
+# Using environment variable to set default metadata behavior
+# Set RETRIEVE_ENABLE_METADATA_DEFAULT=true in your environment
+result = agent.tool.retrieve(
+ text="Tell me about cloud computing"
+ # enableMetadata will default to the environment variable value
+)
+```
+
+### Batch Tool
+
+```python
+import os
+import sys
+
+from strands import Agent
+from strands_tools import batch, http_request, use_aws
+
+# Example usage of the batch with http_request and use_aws tools
+agent = Agent(tools=[batch, http_request, use_aws])
+
+result = agent.tool.batch(
+ invocations=[
+ {"name": "http_request", "arguments": {"method": "GET", "url": "https://api.ipify.org?format=json"}},
+ {
+ "name": "use_aws",
+ "arguments": {
+ "service_name": "s3",
+ "operation_name": "list_buckets",
+ "parameters": {},
+ "region": "us-east-1",
+ "label": "List S3 Buckets"
+ }
+ },
+ ]
+)
+```
+
+### Video Tools
+
+```python
+from strands import Agent
+from strands_tools import search_video, chat_video
+
+agent = Agent(tools=[search_video, chat_video])
+
+# Search for video content using natural language
+result = agent.tool.search_video(
+ query="people discussing AI technology",
+ threshold="high",
+ group_by="video",
+ page_limit=5
+)
+
+# Chat with existing video (no index_id needed)
+result = agent.tool.chat_video(
+ prompt="What are the main topics discussed in this video?",
+ video_id="existing-video-id"
+)
+
+# Chat with new video file (index_id required for upload)
+result = agent.tool.chat_video(
+ prompt="Describe what happens in this video",
+ video_path="/path/to/video.mp4",
+ index_id="your-index-id" # or set TWELVELABS_PEGASUS_INDEX_ID env var
+)
+```
+
+### AgentCore Memory
+```python
+from strands import Agent
+from strands_tools.agent_core_memory import AgentCoreMemoryToolProvider
+
+
+provider = AgentCoreMemoryToolProvider(
+ memory_id="memory-123abc", # Required
+ actor_id="user-456", # Required
+ session_id="session-789", # Required
+ namespace="default", # Required
+ region="us-west-2" # Optional, defaults to us-west-2
+)
+
+agent = Agent(tools=provider.tools)
+
+# Create a new memory
+result = agent.tool.agent_core_memory(
+ action="record",
+ content="I am allergic to shellfish"
+)
+
+# Search for relevant memories
+result = agent.tool.agent_core_memory(
+ action="retrieve",
+ query="user preferences"
+)
+
+# List all memories
+result = agent.tool.agent_core_memory(
+ action="list"
+)
+
+# Get a specific memory by ID
+result = agent.tool.agent_core_memory(
+ action="get",
+ memory_record_id="mr-12345"
+)
+```
+
+### Browser
+```python
+from strands import Agent
+from strands_tools.browser import LocalChromiumBrowser
+
+# Create browser tool
+browser = LocalChromiumBrowser()
+agent = Agent(tools=[browser.browser])
+
+# Simple navigation
+result = agent.tool.browser({
+ "action": {
+ "type": "navigate",
+ "url": "https://example.com"
+ }
+})
+
+# Initialize a session first
+result = agent.tool.browser({
+ "action": {
+ "type": "initSession",
+ "session_name": "main-session",
+ "description": "Web automation session"
+ }
+})
+```
+
+### Handoff to User
+
+```python
+from strands import Agent
+from strands_tools import handoff_to_user
+
+agent = Agent(tools=[handoff_to_user])
+
+# Request user confirmation and continue
+response = agent.tool.handoff_to_user(
+ message="I need your approval to proceed with deleting these files. Type 'yes' to confirm.",
+ breakout_of_loop=False
+)
+
+# Complete handoff to user (stops agent execution)
+agent.tool.handoff_to_user(
+ message="Task completed. Please review the results and take any necessary follow-up actions.",
+ breakout_of_loop=True
+)
+```
+
+### A2A Client
+
+```python
+from strands import Agent
+from strands_tools.a2a_client import A2AClientToolProvider
+
+# Initialize the A2A client provider with known agent URLs
+provider = A2AClientToolProvider(known_agent_urls=["http://localhost:9000"])
+agent = Agent(tools=provider.tools)
+
+# Use natural language to interact with A2A agents
+response = agent("discover available agents and send a greeting message")
+
+# The agent will automatically use the available tools:
+# - discover_agent(url) to find agents
+# - list_discovered_agents() to see all discovered agents
+# - send_message(message_text, target_agent_url) to communicate
+```
+
+### Diagram
+
+```python
+from strands import Agent
+from strands_tools import diagram
+
+agent = Agent(tools=[diagram])
+
+# Create an AWS cloud architecture diagram
+result = agent.tool.diagram(
+ diagram_type="cloud",
+ nodes=[
+ {"id": "users", "type": "Users", "label": "End Users"},
+ {"id": "cloudfront", "type": "CloudFront", "label": "CDN"},
+ {"id": "s3", "type": "S3", "label": "Static Assets"},
+ {"id": "api", "type": "APIGateway", "label": "API Gateway"},
+ {"id": "lambda", "type": "Lambda", "label": "Backend Service"}
+ ],
+ edges=[
+ {"from": "users", "to": "cloudfront"},
+ {"from": "cloudfront", "to": "s3"},
+ {"from": "users", "to": "api"},
+ {"from": "api", "to": "lambda"}
+ ],
+ title="Web Application Architecture"
+)
+
+# Create a UML class diagram
+result = agent.tool.diagram(
+ diagram_type="class",
+ elements=[
+ {
+ "name": "User",
+ "attributes": ["+id: int", "-name: string", "#email: string"],
+ "methods": ["+login(): bool", "+logout(): void"]
+ },
+ {
+ "name": "Order",
+ "attributes": ["+id: int", "-items: List", "-total: float"],
+ "methods": ["+addItem(item): void", "+calculateTotal(): float"]
+ }
+ ],
+ relationships=[
+ {"from": "User", "to": "Order", "type": "association", "multiplicity": "1..*"}
+ ],
+ title="E-commerce Domain Model"
+)
+```
+
+### RSS Feed Management
+
+```python
+from strands import Agent
+from strands_tools import rss
+
+agent = Agent(tools=[rss])
+
+# Subscribe to a feed
+result = agent.tool.rss(
+ action="subscribe",
+ url="https://news.example.com/rss/technology"
+)
+
+# List all subscribed feeds
+feeds = agent.tool.rss(action="list")
+
+# Read entries from a specific feed
+entries = agent.tool.rss(
+ action="read",
+ feed_id="news_example_com_technology",
+ max_entries=5,
+ include_content=True
+)
+
+# Search across all feeds
+search_results = agent.tool.rss(
+ action="search",
+ query="machine learning",
+ max_entries=10
+)
+
+# Fetch feed content without subscribing
+latest_news = agent.tool.rss(
+ action="fetch",
+ url="https://blog.example.org/feed",
+ max_entries=3
+)
+```
+
+### Use Computer
+
+```python
+from strands import Agent
+from strands_tools import use_computer
+
+agent = Agent(tools=[use_computer])
+
+# Find mouse position
+result = agent.tool.use_computer(action="mouse_position")
+
+# Automate adding text
+result = agent.tool.use_computer(action="type", text="Hello, world!", app_name="Notepad")
+
+# Analyze current computer screen
+result = agent.tool.use_computer(action="analyze_screen")
+
+result = agent.tool.use_computer(action="open_app", app_name="Calculator")
+result = agent.tool.use_computer(action="close_app", app_name="Calendar")
+
+result = agent.tool.use_computer(
+ action="hotkey",
+ hotkey_str="command+ctrl+f", # For macOS
+ app_name="Chrome"
+)
+```
+
+### Elasticsearch Memory
+
+**Note**: This tool requires AWS account credentials to generate embeddings using Amazon Bedrock Titan models.
+
+```python
+from strands import Agent
+from strands_tools.elasticsearch_memory import elasticsearch_memory
+
+# Create agent with direct tool usage
+agent = Agent(tools=[elasticsearch_memory])
+
+# Store a memory with semantic embeddings
+result = agent.tool.elasticsearch_memory(
+ action="record",
+ content="User prefers vegetarian pizza with extra cheese",
+ metadata={"category": "food_preferences", "type": "dietary"},
+ cloud_id="your-elasticsearch-cloud-id",
+ api_key="your-api-key",
+ index_name="memories",
+ namespace="user_123"
+)
+
+# Search memories using semantic similarity (vector search)
+result = agent.tool.elasticsearch_memory(
+ action="retrieve",
+ query="food preferences and dietary restrictions",
+ max_results=5,
+ cloud_id="your-elasticsearch-cloud-id",
+ api_key="your-api-key",
+ index_name="memories",
+ namespace="user_123"
+)
+
+# Use configuration dictionary for cleaner code
+config = {
+ "cloud_id": "your-elasticsearch-cloud-id",
+ "api_key": "your-api-key",
+ "index_name": "memories",
+ "namespace": "user_123"
+}
+
+# List all memories with pagination
+result = agent.tool.elasticsearch_memory(
+ action="list",
+ max_results=10,
+ **config
+)
+
+# Get specific memory by ID
+result = agent.tool.elasticsearch_memory(
+ action="get",
+ memory_id="mem_1234567890_abcd1234",
+ **config
+)
+
+# Delete a memory
+result = agent.tool.elasticsearch_memory(
+ action="delete",
+ memory_id="mem_1234567890_abcd1234",
+ **config
+)
+
+# Use Elasticsearch Serverless (URL-based connection)
+result = agent.tool.elasticsearch_memory(
+ action="record",
+ content="User prefers vegetarian pizza",
+ es_url="https://your-serverless-cluster.es.region.aws.elastic.cloud:443",
+ api_key="your-api-key",
+ index_name="memories",
+ namespace="user_123"
+)
+```
+
+### MongoDB Atlas Memory
+
+**Note**: This tool requires AWS account credentials to generate embeddings using Amazon Bedrock Titan models.
+
+```python
+from strands import Agent
+from strands_tools.mongodb_memory import mongodb_memory
+
+# Create agent with direct tool usage
+agent = Agent(tools=[mongodb_memory])
+
+# Store a memory with semantic embeddings
+result = agent.tool.mongodb_memory(
+ action="record",
+ content="User prefers vegetarian pizza with extra cheese",
+ metadata={"category": "food_preferences", "type": "dietary"},
+ connection_string="mongodb+srv://username:password@cluster0.mongodb.net/?retryWrites=true&w=majority",
+ database_name="memories",
+ collection_name="user_memories",
+ namespace="user_123"
+)
+
+# Search memories using semantic similarity (vector search)
+result = agent.tool.mongodb_memory(
+ action="retrieve",
+ query="food preferences and dietary restrictions",
+ max_results=5,
+ connection_string="mongodb+srv://username:password@cluster0.mongodb.net/?retryWrites=true&w=majority",
+ database_name="memories",
+ collection_name="user_memories",
+ namespace="user_123"
+)
+
+# Use configuration dictionary for cleaner code
+config = {
+ "connection_string": "mongodb+srv://username:password@cluster0.mongodb.net/?retryWrites=true&w=majority",
+ "database_name": "memories",
+ "collection_name": "user_memories",
+ "namespace": "user_123"
+}
+
+# List all memories with pagination
+result = agent.tool.mongodb_memory(
+ action="list",
+ max_results=10,
+ **config
+)
+
+# Get specific memory by ID
+result = agent.tool.mongodb_memory(
+ action="get",
+ memory_id="mem_1234567890_abcd1234",
+ **config
+)
+
+# Delete a memory
+result = agent.tool.mongodb_memory(
+ action="delete",
+ memory_id="mem_1234567890_abcd1234",
+ **config
+)
+
+# Use environment variables for connection
+# Set MONGODB_ATLAS_CLUSTER_URI in your environment
+result = agent.tool.mongodb_memory(
+ action="record",
+ content="User prefers vegetarian pizza",
+ database_name="memories",
+ collection_name="user_memories",
+ namespace="user_123"
+)
+```
+
+## 🌍 Environment Variables Configuration
+
+Agents Tools provides extensive customization through environment variables. This allows you to configure tool behavior without modifying code, making it ideal for different environments (development, testing, production).
+
+### Global Environment Variables
+
+These variables affect multiple tools:
+
+| Environment Variable | Description | Default | Affected Tools |
+|----------------------|-------------|---------|---------------|
+| BYPASS_TOOL_CONSENT | Bypass consent for tool invocation, set to "true" to enable | false | All tools that require consent (e.g. shell, file_write, python_repl) |
+| STRANDS_TOOL_CONSOLE_MODE | Enable rich UI for tools, set to "enabled" to enable | disabled | All tools that have optional rich UI |
+| AWS_REGION | Default AWS region for AWS operations | us-west-2 | use_aws, retrieve, generate_image, memory, nova_reels |
+| AWS_PROFILE | AWS profile name to use from ~/.aws/credentials | default | use_aws, retrieve |
+| LOG_LEVEL | Logging level (DEBUG, INFO, WARNING, ERROR) | INFO | All tools |
+
+### Tool-Specific Environment Variables
+
+#### Calculator Tool
+
+| Environment Variable | Description | Default |
+|----------------------|-------------|---------|
+| CALCULATOR_MODE | Default calculation mode | evaluate |
+| CALCULATOR_PRECISION | Number of decimal places for results | 10 |
+| CALCULATOR_SCIENTIFIC | Whether to use scientific notation for numbers | False |
+| CALCULATOR_FORCE_NUMERIC | Force numeric evaluation of symbolic expressions | False |
+| CALCULATOR_FORCE_SCIENTIFIC_THRESHOLD | Threshold for automatic scientific notation | 1e21 |
+| CALCULATOR_DERIVE_ORDER | Default order for derivatives | 1 |
+| CALCULATOR_SERIES_POINT | Default point for series expansion | 0 |
+| CALCULATOR_SERIES_ORDER | Default order for series expansion | 5 |
+
+#### Current Time Tool
+
+| Environment Variable | Description | Default |
+|----------------------|-------------|---------|
+| DEFAULT_TIMEZONE | Default timezone for current_time tool | UTC |
+
+#### Sleep Tool
+
+| Environment Variable | Description | Default |
+|----------------------|-------------|---------|
+| MAX_SLEEP_SECONDS | Maximum allowed sleep duration in seconds | 300 |
+
+#### Tavily Search, Extract, Crawl, and Map Tools
+
+| Environment Variable | Description | Default |
+|----------------------|-------------|---------|
+| TAVILY_API_KEY | Tavily API key (required for all Tavily functionality) | None |
+- Visit https://www.tavily.com/ to create a free account and API key.
+
+#### Exa Search and Contents Tools
+
+| Environment Variable | Description | Default |
+|----------------------|-------------|---------|
+| EXA_API_KEY | Exa API key (required for all Exa functionality) | None |
+- Visit https://dashboard.exa.ai/api-keys to create a free account and API key.
+
+#### Mem0 Memory Tool
+
+The Mem0 Memory Tool supports three different backend configurations:
+
+1. **Mem0 Platform**:
+ - Uses the Mem0 Platform API for memory management
+ - Requires a Mem0 API key
+
+2. **OpenSearch** (Recommended for AWS environments):
+ - Uses OpenSearch as the vector store backend
+ - Requires AWS credentials and OpenSearch configuration
+
+3. **FAISS** (Default for local development):
+ - Uses FAISS as the local vector store backend
+ - Requires faiss-cpu package for local vector storage
+
+4. **Neptune Analytics** (Optional Graph backend for search enhancement):
+ - Uses Neptune Analytics as the graph store backend to enhance memory recall.
+ - Requires AWS credentials and Neptune Analytics configuration
+ ```
+ # Configure your Neptune Analytics graph ID in the .env file:
+ export NEPTUNE_ANALYTICS_GRAPH_IDENTIFIER=sample-graph-id
+
+ # Configure your Neptune Analytics graph ID in Python code:
+ import os
+ os.environ['NEPTUNE_ANALYTICS_GRAPH_IDENTIFIER'] = "g-sample-graph-id"
+
+ ```
+
+| Environment Variable | Description | Default | Required For |
+|----------------------|-------------|---------|--------------|
+| MEM0_API_KEY | Mem0 Platform API key | None | Mem0 Platform |
+| OPENSEARCH_HOST | OpenSearch Host URL | None | OpenSearch |
+| AWS_REGION | AWS Region for OpenSearch | us-west-2 | OpenSearch |
+| NEPTUNE_ANALYTICS_GRAPH_IDENTIFIER | Neptune Analytics Graph Identifier | None | Neptune Analytics |
+| DEV | Enable development mode (bypasses confirmations) | false | All modes |
+| MEM0_LLM_PROVIDER | LLM provider for memory processing | aws_bedrock | All modes |
+| MEM0_LLM_MODEL | LLM model for memory processing | anthropic.claude-3-5-haiku-20241022-v1:0 | All modes |
+| MEM0_LLM_TEMPERATURE | LLM temperature (0.0-2.0) | 0.1 | All modes |
+| MEM0_LLM_MAX_TOKENS | LLM maximum tokens | 2000 | All modes |
+| MEM0_EMBEDDER_PROVIDER | Embedder provider for vector embeddings | aws_bedrock | All modes |
+| MEM0_EMBEDDER_MODEL | Embedder model for vector embeddings | amazon.titan-embed-text-v2:0 | All modes |
+
+
+**Note**:
+- If `MEM0_API_KEY` is set, the tool will use the Mem0 Platform
+- If `OPENSEARCH_HOST` is set, the tool will use OpenSearch
+- If neither is set, the tool will default to FAISS (requires `faiss-cpu` package)
+- If `NEPTUNE_ANALYTICS_GRAPH_IDENTIFIER` is set, the tool will configure Neptune Analytics as graph store to enhance memory search
+- LLM configuration applies to all backend modes and allows customization of the language model used for memory processing
+
+#### Bright Data Tool
+
+| Environment Variable | Description | Default |
+|----------------------|-------------|---------|
+| BRIGHTDATA_API_KEY | Bright Data API Key | None |
+| BRIGHTDATA_ZONE | Bright Data Web Unlocker Zone | web_unlocker1 |
+
+#### Memory Tool
+
+| Environment Variable | Description | Default |
+|----------------------|-------------|---------|
+| MEMORY_DEFAULT_MAX_RESULTS | Default maximum results for list operations | 50 |
+| MEMORY_DEFAULT_MIN_SCORE | Default minimum relevance score for filtering results | 0.4 |
+
+#### Nova Reels Tool
+
+| Environment Variable | Description | Default |
+|----------------------|-------------|---------|
+| NOVA_REEL_DEFAULT_SEED | Default seed for video generation | 0 |
+| NOVA_REEL_DEFAULT_FPS | Default frames per second for generated videos | 24 |
+| NOVA_REEL_DEFAULT_DIMENSION | Default video resolution in WIDTHxHEIGHT format | 1280x720 |
+| NOVA_REEL_DEFAULT_MAX_RESULTS | Default maximum number of jobs to return for list action | 10 |
+
+#### Python REPL Tool
+
+| Environment Variable | Description | Default |
+|----------------------|-------------|---------|
+| PYTHON_REPL_BINARY_MAX_LEN | Maximum length for binary content before truncation | 100 |
+| PYTHON_REPL_INTERACTIVE | Whether to enable interactive PTY mode | None |
+| PYTHON_REPL_RESET_STATE | Whether to reset the REPL state before execution | None |
+| PYTHON_REPL_PERSISTENCE_DIR | Set Directory for python_repl tool to write state file | None |
+
+#### Shell Tool
+
+| Environment Variable | Description | Default |
+|----------------------|-------------|---------|
+| SHELL_DEFAULT_TIMEOUT | Default timeout in seconds for shell commands | 900 |
+
+#### Slack Tool
+
+| Environment Variable | Description | Default |
+|----------------------|-------------|---------|
+| SLACK_DEFAULT_EVENT_COUNT | Default number of events to retrieve | 42 |
+| STRANDS_SLACK_AUTO_REPLY | Enable automatic replies to messages | false |
+| STRANDS_SLACK_LISTEN_ONLY_TAG | Only process messages containing this tag | None |
+
+#### Speak Tool
+
+| Environment Variable | Description | Default |
+|----------------------|-------------|---------|
+| SPEAK_DEFAULT_STYLE | Default style for status messages | green |
+| SPEAK_DEFAULT_MODE | Default speech mode (fast/polly) | fast |
+| SPEAK_DEFAULT_VOICE_ID | Default Polly voice ID | Joanna |
+| SPEAK_DEFAULT_OUTPUT_PATH | Default audio output path | speech_output.mp3 |
+| SPEAK_DEFAULT_PLAY_AUDIO | Whether to play audio by default | True |
+
+#### Editor Tool
+
+| Environment Variable | Description | Default |
+|----------------------|-------------|---------|
+| EDITOR_DIR_TREE_MAX_DEPTH | Maximum depth for directory tree visualization | 2 |
+| EDITOR_DEFAULT_STYLE | Default style for output panels | default |
+| EDITOR_DEFAULT_LANGUAGE | Default language for syntax highlighting | python |
+| EDITOR_DISABLE_BACKUP | Skip creating .bak backup files during edit operations | false |
+
+#### Environment Tool
+
+| Environment Variable | Description | Default |
+|----------------------|-------------|---------|
+| ENV_VARS_MASKED_DEFAULT | Default setting for masking sensitive values | true |
+
+#### Dynamic MCP Client Tool
+
+| Environment Variable | Description | Default |
+|----------------------|-------------|---------|
+| STRANDS_MCP_TIMEOUT | Default timeout in seconds for MCP operations | 30.0 |
+
+#### File Read Tool
+
+| Environment Variable | Description | Default |
+|----------------------|-------------|---------|
+| FILE_READ_RECURSIVE_DEFAULT | Default setting for recursive file searching | true |
+| FILE_READ_CONTEXT_LINES_DEFAULT | Default number of context lines around search matches | 2 |
+| FILE_READ_START_LINE_DEFAULT | Default starting line number for lines mode | 0 |
+| FILE_READ_CHUNK_OFFSET_DEFAULT | Default byte offset for chunk mode | 0 |
+| FILE_READ_DIFF_TYPE_DEFAULT | Default diff type for file comparisons | unified |
+| FILE_READ_USE_GIT_DEFAULT | Default setting for using git in time machine mode | true |
+| FILE_READ_NUM_REVISIONS_DEFAULT | Default number of revisions to show in time machine mode | 5 |
+
+#### Browser Tool
+
+| Environment Variable | Description | Default |
+|----------------------|-------------|---------|
+| STRANDS_DEFAULT_WAIT_TIME | Default setting for wait time with actions | 1 |
+| STRANDS_BROWSER_MAX_RETRIES | Default number of retries to perform when an action fails | 3 |
+| STRANDS_BROWSER_RETRY_DELAY | Default retry delay time for retry mechanisms | 1 |
+| STRANDS_BROWSER_SCREENSHOTS_DIR | Default directory where screenshots will be saved | screenshots |
+| STRANDS_BROWSER_USER_DATA_DIR | Default directory where data for reloading a browser instance is stored | ~/.browser_automation |
+| STRANDS_BROWSER_HEADLESS | Default headless setting for launching browsers | false |
+| STRANDS_BROWSER_WIDTH | Default width of the browser | 1280 |
+| STRANDS_BROWSER_HEIGHT | Default height of the browser | 800 |
+
+#### RSS Tool
+
+| Environment Variable | Description | Default |
+|----------------------|-------------|---------|
+| STRANDS_RSS_MAX_ENTRIES | Default setting for maximum number of entries per feed | 100 |
+| STRANDS_RSS_UPDATE_INTERVAL | Default amount of time between updating rss feeds in minutes | 60 |
+| STRANDS_RSS_STORAGE_PATH | Default storage path where rss feeds are stored locally | strands_rss_feeds (this may vary based on your system) |
+
+#### Retrieve Tool
+
+| Environment Variable | Description | Default |
+|----------------------|-------------|---------|
+| RETRIEVE_ENABLE_METADATA_DEFAULT | Default setting for enabling metadata in retrieve tool responses | false |
+
+#### Video Tools
+
+| Environment Variable | Description | Default |
+|----------------------|-------------|---------|
+| TWELVELABS_API_KEY | TwelveLabs API key for video analysis | None |
+| TWELVELABS_MARENGO_INDEX_ID | Default index ID for search_video tool | None |
+| TWELVELABS_PEGASUS_INDEX_ID | Default index ID for chat_video tool | None |
+
+#### MongoDB Atlas Memory Tool
+
+| Environment Variable | Description | Default |
+|----------------------|-------------|---------|
+| MONGODB_ATLAS_CLUSTER_URI | MongoDB Atlas connection string | None |
+| MONGODB_DEFAULT_DATABASE | Default database name for MongoDB operations | memories |
+| MONGODB_DEFAULT_COLLECTION | Default collection name for MongoDB operations | user_memories |
+| MONGODB_DEFAULT_NAMESPACE | Default namespace for memory isolation | default |
+| MONGODB_DEFAULT_MAX_RESULTS | Default maximum results for list operations | 50 |
+| MONGODB_DEFAULT_MIN_SCORE | Default minimum relevance score for filtering results | 0.4 |
+
+**Note**: This tool requires AWS account credentials to generate embeddings using Amazon Bedrock Titan models.
+
+
+## Contributing ❤️
+
+This is a community-driven project, powered by passionate developers like you.
+We enthusiastically welcome contributions from everyone,
+regardless of experience level—your unique perspective is valuable to us!
+
+### How to Get Started?
+
+1. **Find your first opportunity**: If you're new to the project, explore our labeled "good first issues" for beginner-friendly tasks.
+2. **Understand our workflow**: Review our [Contributing Guide](CONTRIBUTING.md) to learn about our development setup, coding standards, and pull request process.
+3. **Make your impact**: Contributions come in many forms—fixing bugs, enhancing documentation, improving performance, adding features, writing tests, or refining the user experience.
+4. **Submit your work**: When you're ready, submit a well-documented pull request, and our maintainers will provide feedback to help get your changes merged.
+
+Your questions, insights, and ideas are always welcome!
+
+Together, we're building something meaningful that impacts real users. We look forward to collaborating with you!
+
+## License
+
+This project is licensed under the Apache License 2.0 - see the [LICENSE](LICENSE) file for details.
+
+## Security
+
+See [CONTRIBUTING](CONTRIBUTING.md#security-issue-notifications) for more information.
diff --git a/src/strands_tools/json_processor.py b/src/strands_tools/json_processor.py
new file mode 100644
index 00000000..2a15dc72
--- /dev/null
+++ b/src/strands_tools/json_processor.py
@@ -0,0 +1,314 @@
+"""
+JSON processing tool for comprehensive JSON data manipulation and analysis.
+
+This module provides a powerful JSON processing capability with validation,
+formatting, querying, and manipulation operations. It's designed to handle
+various JSON processing scenarios from simple validation to complex data extraction.
+
+Key Features:
+1. JSON Validation:
+ • Syntax validation with detailed error reporting
+ • Type detection and structure analysis
+ • Schema validation support
+
+2. Formatting Operations:
+ • Pretty printing with configurable indentation
+ • Minification for compact representation
+ • Custom formatting options
+
+3. Data Querying:
+ • Dot notation path querying (e.g., 'user.profile.name')
+ • Key extraction and enumeration
+ • Size and structure analysis
+
+4. Data Manipulation:
+ • Safe data extraction and transformation
+ • Type-aware processing
+ • Error handling with graceful fallbacks
+
+Usage with Strands Agent:
+```python
+from strands import Agent
+from strands_tools import json_processor
+
+agent = Agent(tools=[json_processor])
+
+# Validate JSON
+agent.tool.json_processor(action="validate", data='{"name": "John"}')
+
+# Format JSON with custom indentation
+agent.tool.json_processor(action="format", data={"name": "John", "age": 30}, indent=4)
+
+# Query nested data
+agent.tool.json_processor(action="query", data={"user": {"name": "John"}}, query_path="user.name")
+```
+"""
+
+import json
+from typing import Any, Dict, List, Union
+
+from strands import tool
+
+
+@tool
+def json_processor(
+ action: str,
+ data: Union[str, Dict[str, Any], List[Any]] = None,
+ query_path: str = None,
+ indent: int = 2,
+) -> Dict[str, Any]:
+ """
+ Process JSON data with validation, formatting, and querying capabilities.
+
+ This tool provides comprehensive JSON processing including validation,
+ pretty formatting, querying, and basic manipulation operations.
+
+ Args:
+ action (str): The operation to perform:
+ - 'validate': Check if JSON is valid and return type information
+ - 'format': Pretty print JSON with specified indentation
+ - 'minify': Remove whitespace from JSON for compact representation
+ - 'query': Extract value using dot notation path (e.g., 'user.name')
+ - 'keys': Get all keys from JSON object at root or specified path
+ - 'size': Get size/length information about the JSON structure
+ - 'type': Get detailed type information about JSON data
+ data (Union[str, Dict, List]): JSON string or Python object to process
+ query_path (str, optional): Dot notation path for querying (e.g., 'user.profile.name')
+ indent (int, optional): Indentation spaces for formatting (default: 2)
+
+ Returns:
+ Dict[str, Any]: Result containing the processed data and metadata
+
+ Raises:
+ ValueError: If invalid action or required parameters are missing
+
+ Examples:
+ >>> json_processor(action="validate", data='{"name": "John", "age": 30}')
+ {'valid': True, 'message': 'Valid JSON', 'type': 'dict', 'size': 2}
+
+ >>> json_processor(action="format", data='{"name":"John","age":30}', indent=4)
+ {'formatted': '{\n "name": "John",\n "age": 30\n}', 'size': 32}
+
+ >>> json_processor(action="query", data={"user": {"name": "John"}}, query_path="user.name")
+ {'result': 'John', 'path': 'user.name', 'found': True}
+
+ >>> json_processor(action="keys", data={"name": "John", "age": 30})
+ {'keys': ['name', 'age'], 'count': 2, 'type': 'object'}
+ """
+
+ if not action:
+ raise ValueError("Action parameter is required")
+
+ if data is None and action != "validate":
+ raise ValueError(f"Data parameter is required for action '{action}'")
+
+ try:
+ # Parse JSON string to Python object if needed
+ if isinstance(data, str):
+ try:
+ parsed_data = json.loads(data)
+ except json.JSONDecodeError as e:
+ if action == "validate":
+ return {
+ "valid": False,
+ "error": f"Invalid JSON: {str(e)}",
+ "line": getattr(e, "lineno", None),
+ "column": getattr(e, "colno", None),
+ }
+ else:
+ # For non-validate actions, treat as a primitive string value
+ parsed_data = data
+ else:
+ parsed_data = data
+
+ if action == "validate":
+ return {
+ "valid": True,
+ "message": "Valid JSON",
+ "type": type(parsed_data).__name__,
+ "size": len(parsed_data) if hasattr(parsed_data, "__len__") else 1,
+ }
+
+ elif action == "format":
+ try:
+ formatted = json.dumps(parsed_data, indent=indent, ensure_ascii=False, sort_keys=True)
+ return {"formatted": formatted, "size": len(formatted), "lines": formatted.count("\n") + 1}
+ except TypeError as e:
+ raise ValueError(f"Cannot format data as JSON: {str(e)}") from e
+
+ elif action == "minify":
+ try:
+ minified = json.dumps(parsed_data, separators=(",", ":"), ensure_ascii=False)
+ return {
+ "minified": minified,
+ "size": len(minified),
+ "compression_ratio": round(len(minified) / len(json.dumps(parsed_data, indent=2)), 2),
+ }
+ except TypeError as e:
+ raise ValueError(f"Cannot minify data as JSON: {str(e)}") from e
+
+ elif action == "query":
+ if not query_path:
+ raise ValueError("query_path is required for query action")
+
+ # Simple dot notation querying
+ result = parsed_data
+ path_parts = query_path.split(".")
+
+ try:
+ for i, key in enumerate(path_parts):
+ if isinstance(result, dict):
+ if key in result:
+ result = result[key]
+ else:
+ return {
+ "result": None,
+ "path": query_path,
+ "found": False,
+ "error": f"Key '{key}' not found at path '{'.'.join(path_parts[: i + 1])}'",
+ }
+ elif isinstance(result, list):
+ try:
+ index = int(key)
+ if 0 <= index < len(result):
+ result = result[index]
+ else:
+ return {
+ "result": None,
+ "path": query_path,
+ "found": False,
+ "error": f"Index {index} out of range for array of length {len(result)}",
+ }
+ except ValueError:
+ return {
+ "result": None,
+ "path": query_path,
+ "found": False,
+ "error": f"Cannot use key '{key}' on array, expected numeric index",
+ }
+ else:
+ return {
+ "result": None,
+ "path": query_path,
+ "found": False,
+ "error": (
+ f"Cannot query further into {type(result).__name__} "
+ f"at '{'.'.join(path_parts[:i])}'"
+ ),
+ }
+
+ return {
+ "result": result,
+ "path": query_path,
+ "found": True,
+ "type": type(result).__name__,
+ }
+ except Exception as e:
+ return {
+ "result": None,
+ "path": query_path,
+ "found": False,
+ "error": f"Query error: {str(e)}",
+ }
+
+ elif action == "keys":
+ if isinstance(parsed_data, dict):
+ keys = list(parsed_data.keys())
+ return {
+ "keys": keys,
+ "count": len(keys),
+ "type": "object",
+ "nested_objects": sum(1 for v in parsed_data.values() if isinstance(v, dict)),
+ "nested_arrays": sum(1 for v in parsed_data.values() if isinstance(v, list)),
+ }
+ elif isinstance(parsed_data, list):
+ return {
+ "keys": list(range(len(parsed_data))),
+ "count": len(parsed_data),
+ "type": "array",
+ "nested_objects": sum(1 for item in parsed_data if isinstance(item, dict)),
+ "nested_arrays": sum(1 for item in parsed_data if isinstance(item, list)),
+ }
+ else:
+ return {
+ "keys": [],
+ "count": 0,
+ "type": type(parsed_data).__name__,
+ "message": f"Data is a {type(parsed_data).__name__}, not an object or array",
+ }
+
+ elif action == "size":
+
+ def calculate_deep_size(obj):
+ """Calculate the deep size of nested JSON structures."""
+ if isinstance(obj, dict):
+ return sum(calculate_deep_size(v) for v in obj.values()) + len(obj)
+ elif isinstance(obj, list):
+ return sum(calculate_deep_size(item) for item in obj) + len(obj)
+ else:
+ return 1
+
+ if isinstance(parsed_data, dict):
+ return {
+ "keys": len(parsed_data),
+ "type": "object",
+ "deep_size": calculate_deep_size(parsed_data),
+ "max_depth": _calculate_depth(parsed_data),
+ }
+ elif isinstance(parsed_data, list):
+ return {
+ "items": len(parsed_data),
+ "type": "array",
+ "deep_size": calculate_deep_size(parsed_data),
+ "max_depth": _calculate_depth(parsed_data),
+ }
+ else:
+ return {
+ "size": 1,
+ "type": type(parsed_data).__name__,
+ "deep_size": 1,
+ "max_depth": 0,
+ }
+
+ elif action == "type":
+ return {
+ "type": type(parsed_data).__name__,
+ "python_type": str(type(parsed_data)),
+ "is_container": isinstance(parsed_data, (dict, list)),
+ "is_empty": len(parsed_data) == 0 if hasattr(parsed_data, "__len__") else False,
+ "json_serializable": _is_json_serializable(parsed_data),
+ }
+
+ else:
+ raise ValueError(
+ f"Unknown action: {action}. Supported actions: validate, format, minify, query, keys, size, type"
+ )
+
+ except Exception as e:
+ if isinstance(e, ValueError):
+ raise
+ else:
+ raise ValueError(f"Error processing JSON: {str(e)}") from e
+
+
+def _calculate_depth(obj, current_depth=0):
+ """Calculate the maximum depth of nested JSON structures."""
+ if isinstance(obj, dict):
+ if not obj:
+ return current_depth
+ return max(_calculate_depth(v, current_depth + 1) for v in obj.values())
+ elif isinstance(obj, list):
+ if not obj:
+ return current_depth
+ return max(_calculate_depth(item, current_depth + 1) for item in obj)
+ else:
+ return current_depth
+
+
+def _is_json_serializable(obj):
+ """Check if an object is JSON serializable."""
+ try:
+ json.dumps(obj)
+ return True
+ except (TypeError, ValueError):
+ return False
diff --git a/tests/test_json_processor.py b/tests/test_json_processor.py
new file mode 100644
index 00000000..91c61833
--- /dev/null
+++ b/tests/test_json_processor.py
@@ -0,0 +1,274 @@
+"""Tests for the json_processor tool."""
+
+import json
+
+import pytest
+
+from strands_tools.json_processor import json_processor
+
+
+class TestJsonProcessor:
+ """Test cases for json_processor tool."""
+
+ def test_validate_valid_json_string(self):
+ """Test validation of valid JSON string."""
+ result = json_processor(action="validate", data='{"name": "John", "age": 30}')
+
+ assert result["valid"] is True
+ assert result["message"] == "Valid JSON"
+ assert result["type"] == "dict"
+ assert result["size"] == 2
+
+ def test_validate_valid_json_object(self):
+ """Test validation of valid Python dict."""
+ result = json_processor(action="validate", data={"name": "John", "age": 30})
+
+ assert result["valid"] is True
+ assert result["message"] == "Valid JSON"
+ assert result["type"] == "dict"
+ assert result["size"] == 2
+
+ def test_validate_invalid_json_string(self):
+ """Test validation of invalid JSON string."""
+ result = json_processor(action="validate", data='{"name": "John", "age":}')
+
+ assert result["valid"] is False
+ assert "Invalid JSON" in result["error"]
+ assert "line" in result or "column" in result
+
+ def test_format_json_object(self):
+ """Test formatting of JSON object."""
+ data = {"name": "John", "age": 30}
+ result = json_processor(action="format", data=data, indent=2)
+
+ assert "formatted" in result
+ assert "size" in result
+ assert "lines" in result
+ assert result["lines"] > 1 # Should be multi-line
+
+ # Verify it's valid JSON
+ parsed = json.loads(result["formatted"])
+ assert parsed == data
+
+ def test_format_with_custom_indent(self):
+ """Test formatting with custom indentation."""
+ data = {"name": "John", "age": 30}
+ result = json_processor(action="format", data=data, indent=4)
+
+ formatted = result["formatted"]
+ lines = formatted.split("\n")
+ # Check that indentation is 4 spaces
+ assert any(" " in line for line in lines if line.strip())
+
+ def test_minify_json(self):
+ """Test JSON minification."""
+ data = {"name": "John", "age": 30}
+ result = json_processor(action="minify", data=data)
+
+ assert "minified" in result
+ assert "size" in result
+ assert "compression_ratio" in result
+
+ # Verify no unnecessary whitespace
+ minified = result["minified"]
+ assert "\n" not in minified
+ assert " " not in minified # No double spaces
+
+ # Verify it's still valid JSON
+ parsed = json.loads(minified)
+ assert parsed == data
+
+ def test_query_simple_path(self):
+ """Test querying with simple dot notation."""
+ data = {"user": {"name": "John", "age": 30}}
+ result = json_processor(action="query", data=data, query_path="user.name")
+
+ assert result["found"] is True
+ assert result["result"] == "John"
+ assert result["path"] == "user.name"
+ assert result["type"] == "str"
+
+ def test_query_nested_path(self):
+ """Test querying with deeply nested path."""
+ data = {"user": {"profile": {"personal": {"name": "John"}}}}
+ result = json_processor(action="query", data=data, query_path="user.profile.personal.name")
+
+ assert result["found"] is True
+ assert result["result"] == "John"
+ assert result["path"] == "user.profile.personal.name"
+
+ def test_query_array_index(self):
+ """Test querying array elements by index."""
+ data = {"users": [{"name": "John"}, {"name": "Jane"}]}
+ result = json_processor(action="query", data=data, query_path="users.1.name")
+
+ assert result["found"] is True
+ assert result["result"] == "Jane"
+
+ def test_query_nonexistent_key(self):
+ """Test querying non-existent key."""
+ data = {"user": {"name": "John"}}
+ result = json_processor(action="query", data=data, query_path="user.email")
+
+ assert result["found"] is False
+ assert result["result"] is None
+ assert "not found" in result["error"].lower()
+
+ def test_query_out_of_bounds_array(self):
+ """Test querying array with out-of-bounds index."""
+ data = {"users": [{"name": "John"}]}
+ result = json_processor(action="query", data=data, query_path="users.5.name")
+
+ assert result["found"] is False
+ assert "out of range" in result["error"].lower()
+
+ def test_keys_object(self):
+ """Test getting keys from JSON object."""
+ data = {"name": "John", "age": 30, "city": "NYC"}
+ result = json_processor(action="keys", data=data)
+
+ assert result["type"] == "object"
+ assert set(result["keys"]) == {"name", "age", "city"}
+ assert result["count"] == 3
+ assert "nested_objects" in result
+ assert "nested_arrays" in result
+
+ def test_keys_array(self):
+ """Test getting keys (indices) from JSON array."""
+ data = ["a", "b", "c"]
+ result = json_processor(action="keys", data=data)
+
+ assert result["type"] == "array"
+ assert result["keys"] == [0, 1, 2]
+ assert result["count"] == 3
+
+ def test_keys_primitive(self):
+ """Test getting keys from primitive value."""
+ result = json_processor(action="keys", data="hello")
+
+ assert result["type"] == "str"
+ assert result["keys"] == []
+ assert result["count"] == 0
+ assert "not an object or array" in result["message"]
+
+ def test_size_object(self):
+ """Test size calculation for JSON object."""
+ data = {"user": {"name": "John", "details": {"age": 30}}}
+ result = json_processor(action="size", data=data)
+
+ assert result["type"] == "object"
+ assert result["keys"] == 1 # Top-level keys
+ assert result["deep_size"] > result["keys"] # Should count nested elements
+ assert result["max_depth"] > 0
+
+ def test_size_array(self):
+ """Test size calculation for JSON array."""
+ data = [1, 2, [3, 4]]
+ result = json_processor(action="size", data=data)
+
+ assert result["type"] == "array"
+ assert result["items"] == 3
+ assert result["deep_size"] > result["items"]
+ assert result["max_depth"] > 0
+
+ def test_type_information(self):
+ """Test type information extraction."""
+ data = {"name": "John"}
+ result = json_processor(action="type", data=data)
+
+ assert result["type"] == "dict"
+ assert "python_type" in result
+ assert result["is_container"] is True
+ assert result["is_empty"] is False
+ assert result["json_serializable"] is True
+
+ def test_type_empty_container(self):
+ """Test type information for empty container."""
+ result = json_processor(action="type", data=[])
+
+ assert result["type"] == "list"
+ assert result["is_container"] is True
+ assert result["is_empty"] is True
+
+ def test_missing_action_parameter(self):
+ """Test error handling for missing action parameter."""
+ with pytest.raises(ValueError, match="Action parameter is required"):
+ json_processor(action="", data="{}")
+
+ def test_missing_data_parameter(self):
+ """Test error handling for missing data parameter."""
+ with pytest.raises(ValueError, match="Data parameter is required"):
+ json_processor(action="format", data=None)
+
+ def test_missing_query_path(self):
+ """Test error handling for missing query_path in query action."""
+ with pytest.raises(ValueError, match="query_path is required"):
+ json_processor(action="query", data={"name": "John"}, query_path=None)
+
+ def test_invalid_action(self):
+ """Test error handling for invalid action."""
+ with pytest.raises(ValueError, match="Unknown action"):
+ json_processor(action="invalid_action", data={"name": "John"})
+
+ def test_complex_nested_structure(self):
+ """Test with complex nested JSON structure."""
+ data = {
+ "users": [
+ {
+ "id": 1,
+ "profile": {
+ "name": "John",
+ "contacts": {"email": "john@example.com", "phones": ["123-456-7890", "098-765-4321"]},
+ },
+ }
+ ],
+ "metadata": {"total": 1, "page": 1},
+ }
+
+ # Test validation
+ validate_result = json_processor(action="validate", data=data)
+ assert validate_result["valid"] is True
+
+ # Test complex query
+ query_result = json_processor(action="query", data=data, query_path="users.0.profile.contacts.phones.1")
+ assert query_result["found"] is True
+ assert query_result["result"] == "098-765-4321"
+
+ # Test size calculation
+ size_result = json_processor(action="size", data=data)
+ assert size_result["deep_size"] > 10 # Should count all nested elements
+ assert size_result["max_depth"] >= 4 # Deep nesting
+
+ def test_json_string_input(self):
+ """Test processing JSON string input."""
+ json_string = '{"name": "John", "age": 30}'
+ result = json_processor(action="keys", data=json_string)
+
+ assert result["type"] == "object"
+ assert set(result["keys"]) == {"name", "age"}
+
+ def test_malformed_json_string_for_non_validate_action(self):
+ """Test handling of malformed JSON strings in non-validate actions."""
+ # Now malformed JSON strings are treated as primitive strings
+ result = json_processor(action="type", data='{"name": "John", "age":}')
+
+ # Should treat it as a string, not fail
+ assert result["type"] == "str"
+ assert result["json_serializable"] is True # String itself is JSON serializable
+
+ def test_unicode_handling(self):
+ """Test proper Unicode handling in JSON processing."""
+ data = {"name": "José", "city": "São Paulo", "emoji": "🚀"}
+
+ # Test formatting preserves Unicode
+ format_result = json_processor(action="format", data=data)
+ formatted = format_result["formatted"]
+ assert "José" in formatted
+ assert "São Paulo" in formatted
+ assert "🚀" in formatted
+
+ # Test minification preserves Unicode
+ minify_result = json_processor(action="minify", data=data)
+ minified = minify_result["minified"]
+ parsed = json.loads(minified)
+ assert parsed == data
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