# foodnear.me

> AI-native restaurant discovery infrastructure — structured menus for AI agents via Menu Protocol.

foodnear.me is the canonical discovery layer for AI agents seeking reliable, owner-approved restaurant menu data. We provide the Menu Protocol standard (a Schema.org superset optimized for agent parsing) and Agent Discovery Optimization (ADO) scoring to help restaurants become machine-discoverable.

Unlike delivery platforms that scrape menus or lock restaurants into 20-30% fees, foodnear.me offers verified, structured data with cryptographic owner approval. Agents can search restaurants by location, filter by dietary requirements, and retrieve complete menus with allergen declarations, customization options, and preparation times.

Our MCP (Model Context Protocol) endpoint enables Claude, ChatGPT, and other LLM-based agents to natively integrate restaurant discovery without custom API wrappers.

## Services / Features

- **Restaurant Search**: Find verified restaurants by location, cuisine type, dietary filters (vegan, vegetarian, gluten-free, halal, kosher), and ADO score
- **Menu Protocol Menus**: Retrieve full menus in MP v1.0 format with explicit boolean dietary flags, allergen arrays, customization options, and owner signatures
- **ADO Scoring**: Agent Discovery Optimization scores (0-5) indicate data quality, verification status, and machine-readability
- **Restaurant Profiles**: Schema.org/Restaurant JSON-LD with Menu Protocol extensions

## API & Machine Integration

- **MCP Server**: https://foodnear.me/mcp (POST JSON-RPC, GET for discovery)
- **OpenAPI Spec**: https://foodnear.me/openapi.json (OpenAPI 3.1)
- **REST API**: https://foodnear.me/api/v1/*
- **Agent Metadata**: https://foodnear.me/.well-known/agent.json
- **AgentRoot**: https://foodnear.me/.well-known/agentroot.json
- **GPT Plugin**: https://foodnear.me/.well-known/ai-plugin.json

## MCP Tools Available

1. `search_restaurants` - Search by lat/lng, cuisine, dietary filters, ADO score
2. `get_restaurant` - Get detailed restaurant profile with Schema.org markup
3. `get_menu` - Get full Menu Protocol v1.0 formatted menu
4. `get_ado_score_breakdown` - Get scoring breakdown with improvement recommendations

## Key URLs

- Homepage: https://foodnear.me
- OpenAPI Spec: https://foodnear.me/openapi.json
- MCP Endpoint: https://foodnear.me/mcp
- Agent Skill File: https://foodnear.me/SKILL.md
- Documentation: https://foodnear.me/docs
- Menu Protocol Spec: https://github.com/foodnearme/menu-protocol

## Example API Usage

Search for vegan Thai restaurants in NYC:
```
GET /api/v1/search?query=thai&lat=40.7128&lng=-74.0060&dietary=vegan
```

Get menu for a restaurant:
```
GET /api/v1/restaurant/{id}/menu.mp
```

## Data Trust Model

Only restaurants with `verification_status: "verified"` appear in search results. Verified means:
- Restaurant owner has claimed the listing
- Owner has approved the Menu Protocol data
- Approval is cryptographically signed

## Rate Limits

- Unauthenticated: 100 requests/minute
- API key holders: 1000 requests/minute

## Authentication

Public read access is free during beta. Future paid tiers will use API keys (Stripe) and x402 micropayments (USDC on Base) for machine-to-machine access.

## Contact

- Email: api@foodnear.me
- GitHub: https://github.com/foodnearme
- Support: https://foodnear.me/support