MCP server

Food Near Me Official

me.foodnear/foodnear-me

Checked 28 Oct 2026, 09:03 UTC · Connected to it and listed its tools

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

Working at last checkVerified publisher: foodnear.meAbout 6,255 tokens per request once connected9 tools

Website Source

https://foodnear.me/mcp

At a glance

Type: MCP server
Publisher: foodnear.me (verified by the MCP Registry)
Status: Working at last check
Tools: 9
Token cost: About 6,255 tokens per request
Sets up in: Claude Code, Claude Desktop, Codex, Gemini CLI, Cursor, VS Code, Antigravity, ChatGPT, OpenAI API, Grok
Last checked: 28 Oct 2026, 09:03 UTC

Connect it to your agent

Add to Cursor Add to VS Code VS Code Insiders

Opens the app, which shows the server and asks you to confirm. For other apps, copy the setup below.

Claude Code

claude mcp add --transport http foodnear-me https://foodnear.me/mcp

Claude Desktop

Settings → Connectors → Add custom connector, and paste the URL.

Codex (config.toml)

[mcp_servers.foodnear-me]
url = "https://foodnear.me/mcp"

Gemini CLI

{"mcpServers":{"foodnear-me":{"httpUrl":"https://foodnear.me/mcp"}}}

Cursor

{"mcpServers":{"foodnear-me":{"url":"https://foodnear.me/mcp"}}}

VS Code

{"servers":{"foodnear-me":{"type":"http","url":"https://foodnear.me/mcp"}}}

Antigravity

agy mcp add foodnear-me https://foodnear.me/mcp

ChatGPT

Settings → Apps & Connectors → Advanced settings: turn on Developer mode, then Create a connector and paste the URL. Which plans can do this varies.

OpenAI API (Responses tools)

{"type":"mcp","server_label":"foodnear-me","server_url":"https://foodnear.me/mcp","require_approval":"always"}

Grok (xAI API tools)

{"type":"mcp","server_label":"foodnear-me","server_url":"https://foodnear.me/mcp"}

Tools

compare_restaurants_for_diet ~598 tokens
Call this tool when the user wants a side-by-side dietary comparison for 2 to 5 specific restaurants already identified in FNM results. Input Requirements (CRITICAL): `restaurant_ids` MUST be UUIDs copied from prior FNM responses, and `dietary` MUST include at least one supported dietary flag. Optio
explore_area_for_diet ~590 tokens
Call this tool when the user wants a neighborhood overview that surfaces trust tiers explicitly — for example, "what's good for vegan eaters within a mile of this location" or "survey the area around X". Input Requirements (CRITICAL): `location` MUST be `{latitude, longitude}` (Google-style nested o
find_restaurants_along_route ~593 tokens
Call this tool when the user wants route-adjacent dining options between two known coordinates and may care about dietary fit. Input Requirements (CRITICAL): both `origin` and `destination` MUST be `{latitude, longitude}` objects; optional `route_polyline` MUST be a valid encoded polyline if provide
get_ado_score_breakdown ~232 tokens
Call this tool when a restaurant owner, operator, or agent wants to understand why a restaurant is more or less agent-ready. Input Requirements (CRITICAL): `restaurant_id` MUST be a UUID copied from a FNM result. Shows ADO (Agent Discovery Optimization) scoring across menu completeness, location acc
get_menu ~295 tokens
Call this tool only when a `search_restaurants` or `get_restaurant` response has `menu_available: true`. Input Requirements (CRITICAL): `restaurant_id` MUST be a UUID copied from a prior FNM result. Returns the full menu in Menu Protocol v1.0 format with item dietary booleans, declared allergens, cu
get_restaurant ~297 tokens
Call this tool after `search_restaurants` when you need a detailed restaurant profile for a returned `id`. Input Requirements (CRITICAL): `restaurant_id` MUST be a UUID copied from a `search_restaurants` result; do not invent IDs. Returns Schema.org/Restaurant JSON-LD markup plus Menu Protocol exten
get_safety_attestation ~387 tokens
Call this tool when you need a citable, tamper-evident allergy-safety statement for ONE restaurant — for example before telling a user a place is safe for a severe allergy. Input Requirements (CRITICAL): `restaurant_id` MUST be a UUID copied from a prior FNM result. Returns an explicit `safety_tier`
search_restaurants ~2944 tokens
Call this tool when the user wants restaurant or food discovery near a known location and may need menu trust signals. Input Requirements (CRITICAL): provide either flat `lat`/`lng` or Google-style `locationBias.circle.center.latitude`/`longitude`; if the user gave only a vague place name, resolve i
validate_menu_protocol ~319 tokens
Call this tool when validating a draft or exported Menu Protocol payload before submission or integration. Input Requirements (CRITICAL): provide a JSON object in `payload`; set `strict: true` when checking formal spec compliance, and leave strict false for exploratory debugging that should surface

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