foodnear-me
MCP server for AI-native restaurant discovery with three-tier search (verified, menu_indexed, discovered), Menu Protocol menus, and structured menu validation.
README
Food Near Me — MCP Server
Model Context Protocol server for AI-native restaurant discovery — three-tier search (verified → menu_indexed → discovered), Menu Protocol menus, and structured menu validation. Plug into Claude Desktop, Cursor, ChatGPT, or any MCP host in about 30 seconds.
Production endpoint: https://foodnear.me/mcp · 8 tools · 4 resources · 3 prompts · No API key (beta)
Quick start {#quick-start}
1. Add this to your MCP host config
Cursor — ~/.cursor/mcp.json (macOS/Linux) or %USERPROFILE%\.cursor\mcp.json (Windows)
Claude Desktop — ~/Library/Application Support/Claude/claude_desktop_config.json (macOS), %APPDATA%\Claude\claude_desktop_config.json (Windows)
{
"mcpServers": {
"foodnear-me": {
"command": "npx",
"args": ["-y", "mcp-remote", "https://foodnear.me/mcp"]
}
}
}
2. Restart your MCP host
3. Try a prompt
“Find vegan Thai restaurants near Brooklyn Bridge and show me a menu for the top result.”
Your agent should call search_restaurants → get_menu (or get_restaurant first).
What you get
Tools (8)
| Tool | Description |
|---|---|
search_restaurants |
Three-tier geo search by lat/lng — verified → menu_indexed → discovered; check menu_available before get_menu |
get_restaurant |
Restaurant profile with Schema.org JSON-LD + Menu Protocol extensions |
get_menu |
Full Menu Protocol v1.0 menu (dietary flags, allergens, signatures) |
get_ado_score_breakdown |
ADO score factors and improvement recommendations |
validate_menu_protocol |
Validate a Menu Protocol JSON payload before publish |
explore_area_for_diet |
Composite: bucketed neighborhood overview (verified / menu_indexed / discovered); optional dietary filter narrows the verified tier only |
compare_restaurants_for_diet |
Composite: compare 2–5 known restaurants by dietary-eligible menu items and trust tier ranking |
find_restaurants_along_route |
Composite: route-adjacent restaurant discovery between origin/destination with optional dietary ranking |
Resources (4)
| URI | Content |
|---|---|
foodnearme://spec/menu-protocol |
Menu Protocol v1.0 specification |
foodnearme://spec/openapi |
OpenAPI 3.1 spec pointer |
foodnearme://agent/skill |
Agent skill summary |
foodnearme://examples/search-flow |
Example search → menu flow |
Prompts (3)
| Prompt | Args | Guides agent to |
|---|---|---|
find_dinner_near_me |
location (required), cuisine?, dietary? |
search_restaurants → get_menu |
dietary_constrained_menu |
restaurant_id, restrictions |
get_menu with explicit MP flags/allergens |
validate_my_menu |
strict? (true for strict mode) |
validate_menu_protocol |
Configuration
| Setting | Value |
|---|---|
| MCP URL | https://foodnear.me/mcp |
| Transport | HTTP JSON-RPC (POST); discovery via GET /mcp |
| Auth | None during beta (rate limits apply) |
| Registry | me.foodnear/foodnear-me (official MCP Registry) |
Preview / local: Replace the URL with http://localhost:3000/mcp when running npm run dev in apps/web.
Operational tool filter: set FNM_MCP_ENABLED_TOOLS=search_restaurants,get_menu to expose only a comma-separated subset in tools/list and GET /mcp. Leave unset or * to expose all tools. This is for temporary degradation/context control, not privacy or auth.
Architecture
┌─────────────────────┐ POST /mcp (JSON-RPC) ┌─────────────────────────┐
│ MCP host │ ────────────────────────────▶│ apps/web/app/mcp │
│ (Cursor / Claude) │ GET /mcp (discovery) │ Next.js route handler │
└─────────────────────┘ └────────────┬────────────┘
│
▼
┌─────────────────────────┐
│ Supabase + PostGIS │
│ Menu Protocol (MP) │
└─────────────────────────┘
Implementation: apps/web/app/mcp/route.ts · Flow runner: apps/web/lib/mcp/mcp-flow-runner.ts
Tool error contract
Failed tools/call responses include structured metadata in _meta.error:
| Field | Meaning |
|---|---|
code |
VALIDATION_ERROR · NOT_FOUND · UPSTREAM · RATE_LIMITED · UNKNOWN |
message |
What went wrong |
hint |
How to fix the request |
retryable |
Whether the agent should retry |
docs |
https://foodnear.me/docs#quick-start |
Human-readable text is still in content[0].text for hosts that ignore _meta.
Verify
From repo root (with apps/web dev server running for localhost):
# Automated agent flows (14 flows when Supabase + seed configured; 11 without DB)
npm run test:mcp-flows
# Against production
npm run test:mcp-flows:http
# Discovery GETs + MCP tools/list count
npm run smoke:mcp
# Three-tier trust model copy parity (local files)
npm run check:discovery-copy
# Full deploy gate (13 checks + discovery copy on production URL)
npm run preflight -w web
# or: ./apps/web/scripts/deploy-preflight.sh https://foodnear.me
Production monitoring: GitHub Actions workflow MCP Production Smoke runs smoke:mcp daily and on manual dispatch (.github/workflows/mcp-smoke.yml).
Agent discovery
| File | URL |
|---|---|
llms.txt |
https://foodnear.me/llms.txt |
llms-full.txt |
https://foodnear.me/llms-full.txt |
| MCP manifest | https://foodnear.me/.well-known/mcp-server.json |
| AgentRoot | https://foodnear.me/.well-known/agentroot.json |
| Skill file | https://foodnear.me/SKILL.md |
| OpenAPI | https://foodnear.me/openapi.json |
| Web quick reference | https://foodnear.me/docs |
Scripted flows: apps/web/docs/example-agent-flows.md
Data trust model (three-tier search)
search_restaurantsreturns verified → menu_indexed → discovered.- Every result includes
verification_statusandmenu_available. Callget_menuonly whenmenu_availableis true. - Verified — owner-approved MP; authoritative for dietary/allergen claims.
- menu_indexed — automated/public MP menu; cite with caveat — not owner-verified.
- discovered — place only; do not cite menu items.
- Trust progression:
discovered→menu_indexed→verified. See https://foodnear.me/attribution for data sources.
FAQ
Do I need an API key?
No for beta MCP access. Future paid tiers may use API keys or x402 (USDC on Base). See x402-prepaid-spec.md in your local docs/Food Near Me playbook.
Tools not showing after restart?
Confirm the config URL ends with /mcp. Restart the host completely. Run npm run smoke:mcp against your target base URL.
Empty search results?
Beta verified menus are seeded for specific metros (e.g. Williamsburg, NYC). 7 menu_indexed restaurants in Williamsburg have automated menus from website ingest. Discovered place listings cover many US metros — use coordinates in an imported region. Demo coords: 40.7128, -74.006. Run npm run db:seed -w web locally for verified test data.
Cursor vs Claude config path?
See Quick start above — each host uses a different JSON file; only the mcpServers block matters.
How is this different from DoorDash / Uber Eats APIs?
We expose owner-verified Menu Protocol data for agents — not scraped aggregator menus or ordering checkout.
Monorepo layout
This repository ships the MCP server inside the foodnear.me web app:
| Path | Purpose |
|---|---|
apps/web |
Next.js app — MCP at /mcp, landing, API routes |
packages/menu-protocol |
Menu Protocol schema + validators |
database |
Migrations, seeds, schema |
server.json |
Official MCP Registry metadata |
Business strategy and runbooks live in a separate local docs folder (not in this repo) — see your team's docs/Food Near Me playbook.
Development
npm install
cd apps/web && cp .env.example .env.local # Supabase keys
npm run dev # http://localhost:3000
npm run test:mcp-flows # POST localhost:3000/mcp
Operator: menu_indexed website ingest
Promote discovered → menu_indexed via free website/ordering-platform parsers (ChowNow API, order.online, Sauce, Squarespace, BentoBox, Toast, Playwright). Always dry-run first — headless is slow.
cd apps/web
npm run db:probe:menu-batch -- --headless --limit=10
npm run db:import:menu-indexed:website:headless:dry-run -- --limit=10
npm run db:import:menu-indexed:website:headless -- --limit=10 # live
No Uber Eats / DoorDash / Grubhub / RapidAPI scrapers. See apps/web/docs/example-agent-flows.md.
Links
- Website: https://foodnear.me
- GitHub: https://github.com/food-near-me/platform
- Menu Protocol spec: https://github.com/foodnearme/menu-protocol
- Support: https://foodnear.me/support · api@foodnear.me
推荐服务器
Baidu Map
百度地图核心API现已全面兼容MCP协议,是国内首家兼容MCP协议的地图服务商。
Playwright MCP Server
一个模型上下文协议服务器,它使大型语言模型能够通过结构化的可访问性快照与网页进行交互,而无需视觉模型或屏幕截图。
Magic Component Platform (MCP)
一个由人工智能驱动的工具,可以从自然语言描述生成现代化的用户界面组件,并与流行的集成开发环境(IDE)集成,从而简化用户界面开发流程。
Audiense Insights MCP Server
通过模型上下文协议启用与 Audiense Insights 账户的交互,从而促进营销洞察和受众数据的提取和分析,包括人口统计信息、行为和影响者互动。
VeyraX
一个单一的 MCP 工具,连接你所有喜爱的工具:Gmail、日历以及其他 40 多个工具。
graphlit-mcp-server
模型上下文协议 (MCP) 服务器实现了 MCP 客户端与 Graphlit 服务之间的集成。 除了网络爬取之外,还可以将任何内容(从 Slack 到 Gmail 再到播客订阅源)导入到 Graphlit 项目中,然后从 MCP 客户端检索相关内容。
Kagi MCP Server
一个 MCP 服务器,集成了 Kagi 搜索功能和 Claude AI,使 Claude 能够在回答需要最新信息的问题时执行实时网络搜索。
e2b-mcp-server
使用 MCP 通过 e2b 运行代码。
Neon MCP Server
用于与 Neon 管理 API 和数据库交互的 MCP 服务器
Exa MCP Server
模型上下文协议(MCP)服务器允许像 Claude 这样的 AI 助手使用 Exa AI 搜索 API 进行网络搜索。这种设置允许 AI 模型以安全和受控的方式获取实时的网络信息。