The MCP platform that connects beverage brands to every AI assistant.
When a customer says "help me order a latte" to ChatGPT, Claude, Doubao, or any AI assistant — only brands connected via MCP get discovered. This platform is how you get connected.
AI assistants are becoming the new storefront. Hundreds of millions of users already ask AI to help them shop, order food, and make decisions. But most brands have zero presence in this channel — no way to be discovered, recommended, or ordered from.
Traditional APIs don't solve this. AI assistants speak MCP (Model Context Protocol) — the open standard for connecting AI to external tools and data.
Coffee MCP is an open-source, multi-brand MCP platform that lets any coffee, tea, juice, or bakery brand plug into the AI ecosystem — with a YAML config and a brand adapter.
Customer → AI Assistant → MCP Protocol → Coffee MCP → Your Brand's Backend API
What you get:
- 21 consumer-facing tools (browse menu, find stores, order, pay, track)
- 10 B2B enterprise tools (member query, coupons, loyalty, payments)
- 4-tier security model (L0-L3) with rate limiting and transaction safety
- Multi-brand support — one platform, unlimited brands
- Category presets for coffee, tea, juice, and bakery
- Brand onboarding in as little as 5 minutes (CLI) or 30 minutes (AI-assisted)
What it costs: Nothing. MIT licensed. Self-host or extend as you wish.
| Audience | Value |
|---|---|
| Brand CTOs / Digital Leaders | Ship AI ordering for your brand in 2-3 weeks, not 6 months |
| AI/MCP Developers | Production-grade reference for multi-tenant MCP server design |
| Retail Tech Teams | Battle-tested security model (L0-L3) for AI-to-commerce flows |
| Founders | Fork this to build your own vertical MCP platform |
# Install (requires uv + Python 3.13)
uv sync
# Run consumer ordering server (default brand)
uv run coffee-company-toc
# Run with a different brand
BRAND=tea_house uv run coffee-company-toc
# Run B2B enterprise server
uv run coffee-company-mcp
# Run all tests (122 passing)
uv run python tests/test_toc_mcp.py # 89 ToC tests
uv run python tests/test_mcp_real.py # 33 B2B tests
# Initialize a new brand (interactive)
uv run brand-init ┌──────────────────────────────┐
│ AI Assistants (MCP Clients) │
│ ChatGPT / Claude / Doubao │
│ OpenClaw / Cursor / Custom │
└──────────────┬───────────────┘
│ MCP Protocol
┌──────────────▼───────────────┐
│ Gateway (Kong/CloudFlare) │
│ OAuth · Rate Limit · WAF │
└──────────────┬───────────────┘
│
┌────────────────────┼────────────────────┐
│ │
┌────────▼─────────┐ ┌────────────▼───────────┐
│ B2B Server │ │ ToC Server (factory) │
│ 10 read tools │ │ 21 tools · L0-L3 │
│ Kong HMAC auth │ │ Multi-brand YAML │
└──────────────────┘ └────────────┬───────────┘
│
┌────────────▼───────────┐
│ BrandAdapter (ABC) │
│ 21 abstract methods │
├────────────────────────┤
│ DemoAdapter (mock) │
│ YourBrandAdapter (HTTP)│
└────────────────────────┘
Three paths, pick what fits your team:
1. CLI Init (5 min) — Zero-code, YAML-driven:
uv run brand-init
# → Select: coffee / tea / juice / bakery
# → Generates brands/<your_brand>/brand.yaml
# → Done. Run the server.2. AI-Assisted (30 min) — Give your API docs to Claude:
/brand-onboard https://api.yourbrand.com/docs
# → Generates adapter.py + brand.yaml + integration tests
3. Full Custom (1-2 weeks) — Implement BrandAdapter with 21 methods:
class MyBrandAdapter(BrandAdapter):
def nearby_stores(self, city=None, keyword=None):
resp = httpx.get(f"{self.api}/stores", params={"city": city})
return resp.json()["stores"]
# ... 20 more methodsSee Brand Integration Guide for the complete walkthrough.
| Preset | Sizes | Extras | Sweetness |
|---|---|---|---|
| Coffee | tall / grande / venti | espresso shots, syrups | 4 levels |
| Tea | regular / large | boba, pudding, taro | 5 levels |
| Juice | regular / large | chia seeds, nata | 3 levels |
| Bakery | single / combo | gift box | — |
The full ordering journey, from discovery to delivery:
| Group | Tools | Security |
|---|---|---|
| Utility | now_time_info |
L0 |
| Discovery | campaign_calendar · available_coupons · claim_all_coupons |
L1-L2 |
| Account | my_account · my_coupons · my_orders |
L1 |
| Menu | nearby_stores · store_detail · browse_menu · drink_detail · nutrition_info |
L0 |
| Points | stars_mall_products · stars_product_detail · stars_redeem |
L1-L3 |
| Order | delivery_addresses · create_address · store_coupons · calculate_price · create_order · order_status |
L1-L3 |
Four tiers designed for AI-to-commerce, where the AI acts on behalf of the user:
- L0 (60/min) — Public data: menus, stores, nutrition
- L1 (30/min) — User data: account, coupons, orders
- L2 (5/hour) — Write ops: claim coupons, add address
- L3 (10/day) — Transactions: create order, redeem points
- Requires
confirmation_tokenfromcalculate_price - Requires
idempotency_keyto prevent duplicate operations
- Requires
For partner integrations (delivery platforms, loyalty aggregators, payment providers):
| Tool | HTTP API | Description |
|---|---|---|
member_query |
POST /crmadapter/account/query | Query member by mobile/openId/memberId |
member_tier |
POST /crmadapter/account/memberTier | Tier details + stars balance |
member_benefits |
POST /crmadapter/customers/getBenefits | 8 benefit statuses |
member_benefit_list |
POST /crmadapter/asset/coupon/getBenefitList | Coupon list |
coupon_query |
POST /coupon/query | Order coupon status |
coupon_detail |
POST /coupon/detail | Coupon details |
equity_query |
POST /equity/query | Equity distribution status |
equity_detail |
POST /equity/detail | Equity details |
assets_list |
POST /assets/list | All customer assets |
cashier_pay_query |
POST /cashier/payQuery | Payment status |
Claude Desktop / Cursor (stdio):
{
"mcpServers": {
"coffee-toc": {
"command": "uv",
"args": ["run", "--directory", "/path/to/coffee-mcp", "coffee-company-toc"],
"env": { "BRAND": "coffee_company" }
}
}
}OpenClaw / Agent SDK (Streamable HTTP):
coffee = McpServer.http(
url="https://mcp.yourbrand.com/mcp",
headers={"Authorization": f"Bearer {token}"}
)
agent.add_mcp_server(coffee)brands/ # Brand configs (zero-code onboarding)
├── coffee_company/brand.yaml # Default demo brand
└── tea_house/brand.yaml # Example: tea chain
src/coffee_mcp/
├── toc_server.py # ToC server factory (21 tools)
├── brand_config.py # YAML config loader
├── brand_adapter.py # BrandAdapter ABC (21 methods)
├── demo_adapter.py # Mock data adapter
├── brand_init.py # CLI brand initializer
├── presets/catalog.py # Category presets
├── server.py # B2B server (10 tools)
└── cli.py # CLI + REPL
docs/
├── BRAND_INTEGRATION_GUIDE.md # Step-by-step brand onboarding
├── TOC_MCP_PLATFORM_DESIGN.md # Platform design & competitive analysis
├── TOC_SECURITY.md # Security architecture (L0-L3)
└── MCP_API_DESIGN_GUIDE.md # MCP tool design principles
- Brand Integration Guide — Connect your brand step by step
- Platform Design — Architecture, competitive analysis, roadmap
- Security Model — L0-L3 threat model for AI-commerce
- API Design Guide — MCP tool design principles
- Hosted multi-tenant mode (brands self-register, zero-deploy)
- Payment provider integrations (Alipay, WeChat Pay, Stripe)
- Analytics dashboard (AI ordering funnel, conversion tracking)
- More verticals beyond beverage (fast food, convenience stores)
PRs welcome. See the Brand Integration Guide if you want to add support for a new brand or category.
MIT