Food Near Me

me.foodnear由 foodnear.me 发布官方发布者v1.1.0更新于 Nov 2, 2026

AI-native restaurant discovery: verified/menu-indexed/discovered tiers + signed allergy-safety data.

已验证Streamable HTTP可网页运行位置与生活知识与记忆

概览

AI 生成的概览

面向 AI 的餐厅发现远程 MCP 服务器:按信任层级的地理搜索、Menu Protocol 菜单、饮食对比与签名的过敏安全数据。

可以做什么
  • 按坐标搜索餐厅,结果分为已验证、菜单已索引、仅发现三个层级,并查看餐厅资料。
  • 获取完整的 Menu Protocol 菜单(含饮食标记与过敏原),并在发布前校验菜单数据。
  • 按饮食需求探索街区、对比 2 至 5 家餐厅的可用菜品,并查找路线沿途的餐厅。
  • 获取签名的过敏安全声明,以及针对某种过敏需求的最多 3 家人工精选场所。
  • 读取内置资源(Menu Protocol 规范、OpenAPI 指针、代理技能、示例流程),并使用三个引导提示。
适用场景
适合让助手推荐附近餐厅、按菜系或饮食需求筛选、展示菜单,或依据明确的信任层级回答过敏安全问题。更适合能够访问远程 HTTP MCP 端点的宿主。
运行要求
需要可访问的远程 MCP 端点 foodnear.me/mcp(HTTP JSON-RPC);README 建议用 npx mcp-remote 桥接给 Cursor、Claude Desktop 等宿主,这需要 Node.js。测试期无需 API 密钥,但有速率限制。数据覆盖集中在已预置的美国都市区。
安装前请注意
测试期无需认证,请求为未认证且受速率限制;未来付费层级可能使用 API 密钥或加密货币支付。菜单数据信任度不一:只有已验证菜单对饮食与过敏原声明具有权威性,仅发现的场所不应引用其菜品。未整理的场所其过敏安全声明可能为未知并仅为提示性。

安装

在 SourceWeft 中

  1. 打开 控制台中的 Food Near Me,将其添加到工作区。
  2. 为需要使用其工具的对话启用该服务。

Web executable,通过 Streamable HTTP。 远程服务在工作区中配置后即可从网页运行时运行。

交给智能体安装

把这段提示词粘贴到 Claude Code、Codex、Cursor 等智能体,或 SourceWeft 对话中。智能体会读取该服务器的安装指南,先向你展示发布者、端点和登录方式,经你同意后再把它添加到你的客户端。密钥不会写进任何命令。

阅读 https://sourceweft.com/mcp/me.foodnear%2Ffoodnear-me/install.md,并按其中说明把这个 MCP 服务器接入我的客户端。修改任何配置之前,先告诉我它的发布者、端点和登录方式,等我确认。不要把 API 密钥写进命令或我会提交的文件;问我密钥应从哪里获取。

查看智能体所依据的安装指南

其他 MCP 客户端

把它添加到你客户端的 mcpServers 配置中。

{
  "mcpServers": {
    "food-near-me": {
      "type": "http",
      "url": "https://foodnear.me/mcp"
    }
  }
}

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.

[MCP Registry]

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)

json
{  "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)

ToolDescription
search_restaurantsThree-tier geo search by lat/lng — verified → menu_indexed → discovered; check menu_available before get_menu
get_restaurantRestaurant profile with Schema.org JSON-LD + Menu Protocol extensions
get_menuFull Menu Protocol v1.0 menu (dietary flags, allergens, signatures)
get_ado_score_breakdownADO score factors and improvement recommendations
validate_menu_protocolValidate a Menu Protocol JSON payload before publish
explore_area_for_dietComposite: bucketed neighborhood overview (verified / menu_indexed / discovered); optional dietary filter narrows the verified tier only
compare_restaurants_for_dietComposite: compare 2–5 known restaurants by dietary-eligible menu items and trust tier ranking
find_restaurants_along_routeComposite: route-adjacent restaurant discovery between origin/destination with optional dietary ranking
get_safety_attestationSigned (Ed25519, fnm-safety-v1) allergy-safety tier statement for one restaurant; unknown + advisory when uncurated
find_safe_places_near_meThe pack: at most 3 human-curated places for an allergy need, each with curator_checked_at; left_out cut line; explicit empty_state

Resources (4)

URIContent
foodnearme://spec/menu-protocolMenu Protocol v1.0 specification
foodnearme://spec/openapiOpenAPI 3.1 spec pointer
foodnearme://agent/skillAgent skill summary
foodnearme://examples/search-flowExample search → menu flow

Prompts (3)

PromptArgsGuides agent to
find_dinner_near_melocation (required), cuisine?, dietary?search_restaurants → get_menu
dietary_constrained_menurestaurant_id, restrictionsget_menu with explicit MP flags/allergens
validate_my_menustrict? (true for strict mode)validate_menu_protocol

Configuration

SettingValue
MCP URLhttps://foodnear.me/mcp
TransportHTTP JSON-RPC (POST); discovery via GET /mcp
AuthNone during beta (rate limits apply)
Registryme.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:

FieldMeaning
codeVALIDATION_ERROR · NOT_FOUND · UPSTREAM · RATE_LIMITED · UNKNOWN
messageWhat went wrong
hintHow to fix the request
retryableWhether the agent should retry
docshttps://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):

bash
# Automated agent flows (14 flows when Supabase + seed configured; 11 without DB)npm run test:mcp-flows
# Against productionnpm run test:mcp-flows:http
# Discovery GETs + MCP tools/list countnpm 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

Scripted flows: apps/web/docs/example-agent-flows.md


Data trust model (three-tier search)

  • search_restaurants returns verified → menu_indexed → discovered.
  • Every result includes verification_status and menu_available. Call get_menu only when menu_available is 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:

PathPurpose
apps/webNext.js app — MCP at /mcp, landing, API routes
packages/menu-protocolMenu Protocol schema + validators
databaseMigrations, seeds, schema
server.jsonOfficial 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

bash
npm installcd apps/web && cp .env.example .env.local   # Supabase keysnpm run dev                                 # http://localhost:3000npm 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.

bash
cd apps/webnpm run db:probe:menu-batch -- --headless --limit=10npm run db:import:menu-indexed:website:headless:dry-run -- --limit=10npm 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

来源:README.md,提交 7c5156a

工具

0
SourceWeft 尚未读取此服务器的工具。

版本历史

1
  1. v1.1.0最新Sep 16, 2026

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