# Food Near Me (remote · foodnear.me)

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

- Trust score: 64/100 (medium)
- Change this week: +7
- Registry status: active
- Liveness: live
- Owner verified: no
- Last scored: 2026-08-03

## Components

- remote · `foodnear.me`: 64/100 (this document), [markdown](https://verifymcp.io/servers/me-foodnear-foodnear-me/foodnear.md), [page](https://verifymcp.io/servers/me-foodnear-foodnear-me/foodnear)

## Channel facts

- Endpoint: `https://foodnear.me/mcp`
- Transports: `streamable-http`
- Auth: `none`
- Version: `1.1.0`

## Trust breakdown

How this component scores in each security and reliability category. Every signal is checked automatically against the live server, and we only credit what we can confirm. Scores are 0–100 per category. Scoring method: https://verifymcp.io/docs/scoring (what has changed: https://verifymcp.io/docs/scoring/changelog)

Scored 2026-08-03.

- **Endpoint Security**: 83/100
  - The endpoint's TLS certificate is valid, in date, and uses a strong key.
  - No authorisation is required to call this server. Every tool declares its destructiveHint and none is destructive, so open access doesn't expose one.
  - HTTPS is enforced; there's no plaintext access path.
  - The HSTS (Strict-Transport-Security) header is present.
  - DNSSEC is configured correctly; the domain's records validate against the full chain to the root.
- **Transport & Reachability**: 0/100
  - Transport check failed: the endpoint responded but didn't complete an MCP handshake over streamable-http.
- **Schema Quality & AI Usability**: 78/100
  - 100% of prompts and resources have a non-trivial description (not blank, and not just the item's name).
  - AI-judged instruction clarity (excellent).
  - Context-footprint check failed: tool/resource definitions use about 2303 tokens (~177/item across 13 items; 9 tools + 4 resources), over budget; trim descriptions and params.
  - Usage-examples check failed: none of the tools include examples.
- **Stability & Change Management**: 27/100
  - Stability observed for 8 of 30 days with no destabilising changes; credit accrues until the full window elapses.
- **Tool Coverage**: 100/100
  - 100% of tools have a non-trivial description (not blank, and not just the tool's name).
  - 100% of tool parameters carry a description.
- **Capabilities**: 20/100
  - Spec-recency check failed: implements MCP spec 2024-11-05; the latest is 2026-07-28.

## Install

### Claude

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

### Codex

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

### opencode

```json
{
  "$schema": "https://opencode.ai/config.json",
  "mcp": {
    "me-foodnear-foodnear-me": {
      "type": "remote",
      "url": "https://foodnear.me/mcp",
      "enabled": true
    }
  }
}
```

### OpenClaw

```bash
openclaw mcp add me-foodnear-foodnear-me --url https://foodnear.me/mcp --transport streamable-http
```

### Hermes

```yaml
mcp_servers:
  me-foodnear-foodnear-me:
    url: "https://foodnear.me/mcp"
```

### Other

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

The mcpServers block is a cross-client convention. Remote transports vary, so check your client's docs.

## Changelog

Every change recorded for this component, newest first. Days that predate change tracking, or that we cannot explain, say so: "we were watching and nothing happened" and "we were not watching" are different claims.

### 2026-08-03 (score 64, +1)

No change was recorded against any check on this day. Stability & Change Management went from 23 to 27. That category is still filling its 30-day observation window: 7 days of observed history at the previous scan, 8 at this one. The score rises as the window fills, whether or not the server changes.

### 2026-08-01 (score 63, +1)

No change was recorded against any check on this day. Stability & Change Management went from 17 to 20. That category is still filling its 30-day observation window: 5 days of observed history at the previous scan, 6 at this one. The score rises as the window fills, whether or not the server changes.

### 2026-07-31 (score 62, +3)

- [functional] We updated how we score, so this day's move reflects our rubric, not a change to the server

### 2026-07-30 (score 59, +1)

- [functional] We updated how we score, so this day's move reflects our rubric, not a change to the server

### 2026-07-28 (score 58, +1)

No change was recorded against any check on this day. Stability & Change Management went from 3 to 7. That category is still filling its 30-day observation window: 1 days of observed history at the previous scan, 2 at this one. The score rises as the window fills, whether or not the server changes.

### 2026-07-27 (score 57, +1)

- [functional] We updated how we score, so this day's move reflects our rubric, not a change to the server

### 2026-07-26 (score 56)

First indexed and scored.

## MCP tools (9)

### `search_restaurants` (~300 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 it before calling or include a specific city/region in `textQuery`/`query`. Returns verified venues first, then menu_indexed (automated MP menu with caveat), then discovered (place only). MUST use `menu_available` and `verification_status` on each result; call `get_menu` only when `menu_available` is true. PREFER verified results for dietary/allergen answers. NOTE: `dietary` and `min_ado_score` filters only apply to the verified tier; menu_indexed and discovered rows are returned unfiltered for those criteria — the response echoes `filters.applied_to: ["verified"]` and a `filters.note` so agents know to re-filter at item level using `get_menu`. Non-verified rows include a structured `claim_invitation` (url, message, audience="owner_or_advocate", reason); SHOULD surface this when the user is the restaurant's owner or might know them, when the user asks why the listing lacks a verified menu, or when explaining tier differences — never as an unsolicited CTA. Attribute grounded output using `citation` or `attribution`.

### `get_restaurant` (~186 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 extensions including ADO score, verification status, menu availability, payment methods, and dietary certifications. MUST inspect `menu_available` before calling `get_menu`; if false, use the claim link instead of citing menu items. Non-verified responses include a top-level `claim_invitation` (url, message, audience="owner_or_advocate", reason) the agent SHOULD share if the user is the owner or might know them. Attribute grounded output using `citation` or `attribution`.

Input parameters:

- `restaurant_id` (string, required): Restaurant UUID (from search_restaurants results)

### `get_menu` (~191 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, customization options, preparation times, trust notice, and signature metadata. PREFER `verification_status: "verified"` for dietary/allergen answers. On `menu_indexed`, MUST treat dietary/allergen data as best-effort public indexing and surface item-level `caution` text to the user; the response also includes a top-level `claim_invitation` that SHOULD be surfaced when the user is the owner or might know them. Attribute grounded output using `citation` or `attribution`.

Input parameters:

- `restaurant_id` (string, required): Restaurant UUID (from search_restaurants results)

### `get_ado_score_breakdown` (~141 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 accuracy, data freshness, protocol compliance, verification status, and media context. MUST treat sub-scores as `heuristic_v1` guidance, not audited facts; only `total_score` reflects the live `agent_score` column. Attribute grounded output using `citation` or `attribution`.

Input parameters:

- `restaurant_id` (string, required): Restaurant UUID

### `get_safety_attestation` (~278 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`. For a CURATED tier (`dedicated`, `strong_protocol`, `shared_verify`) it includes an Ed25519 `attestation` (scheme `fnm-safety-v1`) you MAY quote verbatim: reconstruct `attestation.canonical`, sha256 it to reproduce `attestation.hash`, then verify `attestation.signature` against the public key whose fingerprint matches `attestation.key_fingerprint` at `attestation.public_key_url`. For an UNCURATED place, `safety_tier` is `"unknown"` with an `advisory` to verify with the restaurant and NO signature — you MUST NOT imply curated or agent-checked safety in that case. `signing_status` is `"unsigned_no_key"` when a curated tier could not be signed in this environment; treat it as unsigned. Never infer or upgrade a tier this tool did not return. Attribute grounded output using `citation` or `attribution`.

Input parameters:

- `restaurant_id` (string, required): Restaurant UUID

### `validate_menu_protocol` (~175 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 warnings without rejecting usable drafts. Returns validation errors, schema warnings, Schema.org compliance gaps, and recommendations for improving ADO score. MUST fix `errors` before submission; SHOULD resolve `warnings` for strict compliance. Attribute grounded output using `citation` or `attribution`.

Input parameters:

- `payload` (object, required): The Menu Protocol JSON payload to validate. Should include version, domain, restaurant, and menu objects.
- `strict` (boolean): If true, schema warnings are promoted to errors and `valid` reflects strict spec compliance. Default: false (lenient mode).

### `explore_area_for_diet` (~339 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 object). Returns three tier buckets — `verified`, `menu_indexed`, `discovered` — each trimmed to `top_n_per_tier` (default 3, max 10). When `dietary` is set, the filter only narrows the `verified` bucket, matching `search_restaurants` semantics; `tier_counts` always reflects the full result set and `next_steps` flags any empty bucket. PREFER `verified` for dietary/allergen answers; MUST check `menu_available` before calling `get_menu` on any returned id. Non-verified entries include a structured `claim_invitation` the agent SHOULD surface when the user is the owner or might know them. Attribute grounded output using `citation` or `attribution`.

Input parameters:

- `dietary` (array): Optional dietary filter applied with AND logic. Only narrows the verified tier; menu_indexed and discovered rows pass through unfiltered (matches search_restaurants semantics).
- `location` (object, required): Center of the area to explore (Google-style nested latitude/longitude).
- `radius_meters` (number): Default 1000 m (about 0.62 mi). Max 80467 m (50 miles).
- `top_n_per_tier` (integer): Default 3, max 10. Each tier bucket is trimmed independently.

### `compare_restaurants_for_diet` (~317 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. Optional `user_location` (`{latitude, longitude}`) enables per-row `distance_meters` and uses distance as the final tiebreaker after item count and trust tier. The tool chains `get_restaurant` and `get_menu` internally, then ranks by dietary-eligible item count, trust tier (`verified` preferred over `menu_indexed`, then `discovered`), and optionally distance. PREFER verified-tier winners for authoritative dietary/allergen answers; MUST treat menu_indexed matches as best-effort public indexing with caveats. Non-verified entries carry a structured `claim_invitation` SHOULD be surfaced when the user could help with ownership. Attribute grounded output using `citation` or `attribution`.

Input parameters:

- `dietary` (array, required): Dietary flags applied with AND logic at item level.
- `restaurant_ids` (array, required): UUIDs copied from prior search_restaurants results.
- `user_location` (object): Optional caller location. When set, each compared restaurant carries `distance_meters` (great-circle from this point) and distance becomes the final tiebreaker after item_count and trust tier. Restau…

### `find_restaurants_along_route` (~268 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 provided. The tool samples waypoints along the corridor, merges nearby search matches, then ranks by dietary match count (when requested), trust tier, and route proximity. SHOULD provide `route_polyline` from your routing source for tighter ranking; otherwise fallback is a local great-circle approximation. MUST check `menu_available` before calling `get_menu` on returned ids. Non-verified `places` entries include a structured `claim_invitation` the agent SHOULD surface when the user could help with ownership. Attribute grounded output using `citation` or `attribution`.

Input parameters:

- `destination` (object, required): Route end coordinates.
- `dietary` (array): Optional dietary filter. When set, route ranking prefers restaurants with more matching menu items.
- `max_results` (integer): Default 5, max 20.
- `origin` (object, required): Route start coordinates.
- `route_polyline` (string): Optional encoded polyline from the caller's routing source (Google encoded polyline format).

## Diagnostics

Captured diagnostic sections: TLS, DNSSEC, Authorisation, Transports. The full working is on the page: https://verifymcp.io/servers/me-foodnear-foodnear-me/foodnear#diagnostics

## Score history

- 2026-08-03: 64
- 2026-08-02: 63
- 2026-08-01: 63
- 2026-07-31: 62
- 2026-07-30: 59
- 2026-07-29: 58
- 2026-07-28: 58
- 2026-07-27: 57
- 2026-07-26: 56

## Links

- Remote endpoint: https://foodnear.me/mcp
- Repository: https://github.com/food-near-me/platform
- Website: https://foodnear.me/
- Changelog RSS feed: https://verifymcp.io/servers/me-foodnear-foodnear-me/foodnear/changelog.xml
- Changelog JSON feed: https://verifymcp.io/servers/me-foodnear-foodnear-me/foodnear/changelog.json
- HTML version of this page: https://verifymcp.io/servers/me-foodnear-foodnear-me/foodnear