Maître d'MCP
Enables users to sync their restaurant reservations by generating one-click links to add booking details directly to Google Calendar.
Integrates with the Google Places API to provide restaurant discovery features, including ratings, reviews, and proximity-based searches.
Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@Maître d'MCP Find a quiet Italian spot for 4 on Saturday at 7pm that handles peanut allergies."
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
Maître d'MCP
An MCP-based that provides a set of tools to discover restaurants, checks availability, and books reservations through natural conversation with chat agents that integrate with MCP.
Note: Three MCP servers for Resy reservations already exist in the ecosystem (see Prior Art), but none combine Google Places discovery, weather integration, dining companion tracking, and real booking across both Resy and OpenTable — that gap is this project's value proposition.
What It Does
You: "Book me a quiet Italian place near home for Saturday at 7"
Claude: I found 3 Italian spots within 10 min walk of your home:
1. Carbone (4.7★) - 6:30 PM, 9:15 PM on Resy
2. L'Artusi (4.5★) - 7:00 PM on OpenTable
3. Via Carota (4.6★) - 8:45 PM on Resy
Your wife has a peanut allergy - I've verified these don't
have nut-heavy menus. Which would you like?
You: "L'Artusi at 6:30"
Claude: ✓ Booked! Carbone, Saturday 6:30 PM, 2 people
Confirmation: RESY-ABC123
Add to Google Calendar: https://calendar.google.com/calendar/render?...Related MCP server: opentable-mcp
Key Features
Feature | Description |
Smart Discovery | Google Places ratings + reviews, filtered by your preferences |
Multi-Platform Booking | Resy (automated), OpenTable (automated) |
Dietary Awareness | Remembers your restrictions and your dining companions' |
Group Dining | Save people (with their restrictions) and groups for easy booking |
Recency Tracking | Won't suggest Mexican if you had it yesterday |
Weather Aware | No outdoor seating suggestions in winter/rain |
Visit History | Tracks where you've been, resurfaces favorites |
Calendar Sync | Add reservations to Google Calendar with one click |
Cost Tracking | Monitor your API usage costs |
Resilient | Retry with backoff, circuit breakers, graceful fallbacks |
Remote Hosting | Docker + Cloudflare Tunnel for access from any device over HTTPS |
Quick Start
1. Prerequisites
Python 3.11+
Claude Desktop installed
Google Cloud API key (Places API enabled)
2. Clone & Install
git clone <repo>
cd restaurant-mcp
python3 -m venv .venv
source .venv/bin/activate
pip install -e ".[dev]"
playwright install chromium3. Run Setup
The interactive setup encrypts all secrets into the local database and generates your Claude Desktop config:
source .venv/bin/activate
python -m src.setupYou will be prompted for:
Google API Key (required) — Google Cloud Console → Places API (New)
OpenWeather API Key (optional) — openweathermap.org (free tier: 1000/day)
Resy credentials (optional) — for automated Resy booking
OpenTable session (optional) — CSRF token + browser cookies for OpenTable booking
The script outputs a ready-to-paste Claude Desktop config with a single RESTAURANT_MCP_KEY env var.
OpenTable's API requires authenticated browser session cookies to bypass bot protection. Without this step, OpenTable availability checks and bookings will fail.
During python -m src.setup, when you provide an OpenTable email you'll be prompted for:
OpenTable x-csrf-token — from browser DevTools
OpenTable Cookie header — from browser DevTools
How to get these values (re-run setup when cookies expire, typically every few days):
Open https://www.opentable.com in Chrome and log in
Open DevTools (
Cmd+Option+Ion macOS,F12on Windows)Go to the Network tab
Navigate to any restaurant page (e.g. search for "Carbone" and click it)
In the Network list, click any request to
www.opentable.comFind any request to a
/dapi/endpoint (POST) and copy thex-csrf-tokenheader valueIn the Headers tab, copy the full
Cookieheader valueSave the cookie value into a temp file that can be referenced in the
src.setupcommand. This is due to the paste size being too large.
Why is this needed? OpenTable uses Cloudflare bot protection that blocks plain HTTP requests. By storing your browser's session cookies, the MCP server can make API calls as your authenticated session. The cookies are encrypted at rest using the same Fernet encryption as all other credentials.
4. Configure Claude Desktop
Copy the JSON output from step 3 into your Claude Desktop MCP config file:
macOS:
~/Library/Application Support/Claude/claude_desktop_config.jsonWindows:
%APPDATA%\Claude\claude_desktop_config.json
The config should look like:
{
"mcpServers": {
"restaurant": {
"command": "/path/to/restaurant-mcp/.venv/bin/python",
"args": ["-m", "src"],
"cwd": "/path/to/restaurant-mcp",
"env": {
"RESTAURANT_MCP_KEY": "your-generated-master-key"
}
}
}
}Replace paths with the actual paths from the setup output.
5. (Optional) Remote Hosting via Docker
For access from Claude.ai, Claude Code, or mobile — run the server on a home server / NAS / Raspberry Pi:
# Copy env template and fill in values from setup
cp .env.example .env
# Edit .env — add MCP_AUTH_TOKEN, RESTAURANT_MCP_KEY, and TUNNEL_TOKEN
# Build and start
docker compose up --build -d
# Verify
curl http://localhost:8000/health
# → {"status":"ok"}Then configure Claude Desktop for remote access:
{
"mcpServers": {
"restaurant": {
"url": "https://mcp.yourdomain.com/mcp",
"headers": {
"Authorization": "Bearer YOUR_MCP_AUTH_TOKEN"
}
}
}
}Or Claude Code:
claude mcp add restaurant --transport streamable-http \
--url https://mcp.yourdomain.com/mcp \
--header "Authorization: Bearer YOUR_TOKEN"See Remote Hosting Spec for full Cloudflare Tunnel setup instructions.
6. Restart Claude Desktop
Quit and reopen Claude Desktop. You should see the restaurant tools available (hammer icon in the chat input).
7. First Run — Set Up Your Profile
Start a conversation with Claude:
You: "Help me set up my restaurant preferences"
Claude: Let's get you set up! First, what's your home address?
(I'll use this as "near home" when you search)
You: "123 Main St, New York, NY 10001"
Claude: Got it. Where do you work?
You: "456 Park Ave, New York, NY 10022"
Claude: What are your favorite cuisines?
You: "Italian, Japanese, Mexican"
Claude: Any dietary restrictions?
You: "None for me"
Claude: ✓ Profile saved! You're all set.8. Add Dining Companions
You: "Add my wife — she has a nut allergy and a seed allergy"
Claude: ✓ Saved! I'll remember her allergies when searching restaurants.
You: "Create a group called 'date night' with my wife"
Claude: ✓ Group 'date night' created. Merged restrictions: nut allergy, seed allergy.Security Model
Layer | Protection |
Setup script |
|
Single secret | One master key ( |
Encryption at rest | Fernet (AES-128-CBC) via PBKDF2-derived key; all config in SQLite |
Legacy mode |
|
Resy password | NOT persisted after authentication — only email + auth token stored |
OpenTable session | CSRF token + browser cookies stored encrypted; no password needed |
File permissions | Credentials dir 0o700, all files 0o600 |
Install the optional keyring dependency for OS keyring support (legacy mode):
pip install -e ".[security]"Example Prompts
Discovery
"Find Italian restaurants near home"
"What's good for dinner near work tonight?"
"Show me highly-rated sushi places within walking distance"
"Find restaurants good for a group of 6 near Union Square"
Booking
"Check availability at Carbone for Saturday at 7 PM, party of 2"
"Book L'Artusi for Friday at 8, party of 4"
"What reservations do I have coming up?"
"Cancel my reservation at Via Carota"
Group Dining
"Find a restaurant for date night this Saturday"
"Search for a place that works for the whole family — remember everyone's allergies"
Recommendations
"What should we try tonight? We haven't been out in a week"
"Recommend something new — I'm tired of Italian"
"What's good for outdoor dining today?" (checks weather automatically)
History & Preferences
"Log that we went to Lilia last night — it was amazing, 5 stars"
"Where have we eaten in the last month?"
"Update my preferences — add Thai to my favorite cuisines"
"Blacklist TGI Friday's — never suggest it again"
Cost Tracking
"How much have I spent on API calls this month?"
MCP Tools (22 total)
Tool | Purpose |
| First-run profile setup (home, work, cuisines, dietary) |
| View current preferences |
| Change specific preferences |
| Add/update/remove dining companions |
| Show all saved companions |
| Create/update/remove groups |
| Show all saved groups |
| Block/unblock restaurants |
| Find restaurants by cuisine, location, rating |
| Check time slots across Resy + OpenTable |
| Book a table (with calendar link) |
| Cancel a booking |
| View upcoming reservations |
| Save Resy login (encrypted) |
| Save OpenTable login (encrypted) |
| Record a restaurant visit |
| Rate a past visit |
| View dining history |
| Get personalized suggestions |
| Find restaurants for a group (merged dietary needs) |
| View API usage costs and cache stats |
API Costs
API | Cost | Usage |
Google Places | ~$17/1000 detail calls | Primary discovery |
OpenWeatherMap | Free (1000/day) | Weather context |
Resy | Free (unofficial) | Booking |
OpenTable | Free (DAPI + browser session) | Booking |
Estimated monthly cost for heavy use: $3-8 (with caching)
Use api_costs to monitor your spending at any time.
Architecture
Technical Stack
Language: Python 3.11+
Framework: FastMCP — auto-generates tool schemas from type hints
Transport: stdio (local) or streamable-http (remote hosting)
Storage: SQLite (local, WAL mode) with aiosqlite
Auth: Bearer token via FastMCP's
TokenVerifier(constant-time comparison)Browser Automation: Playwright (for auth + OpenTable)
APIs: Google Places (New), OpenWeatherMap, Resy (unofficial), OpenTable (automation)
Calendar: Google Calendar URL generation (zero-config)
Containerization: Docker + Cloudflare Tunnel for remote hosting
Resilience: tenacity (retry), custom CircuitBreaker, InMemoryCache (LRU + TTL)
Resilience Features
Retry with exponential backoff — transient errors (429, 5xx) are automatically retried up to 3 times
Circuit breakers — per-service (Resy, Google Places, OpenTable, Weather) to prevent hammering failed APIs
3-layer booking fallback — Resy API -> OpenTable Playwright -> deep links with manual instructions
In-memory caching — LRU cache with TTL for search results, reducing API costs
User-friendly errors — all exceptions are mapped to actionable messages for Claude to relay
Project Structure
restaurant-mcp/
├── .ai/
│ ├── AGENTS.md # Agent instructions
│ └── ENGINEERING-STANDARDS.md # Code patterns, testing mandate
├── docs/
│ ├── specs/ # EPICs, architecture plan, research
│ └── adr/ # Architecture Decision Records
├── scripts/
│ ├── validate.sh # Full validation: lint + test + coverage
│ ├── test.sh # Run tests with coverage
│ └── lint.sh # Ruff linting only
├── src/
│ ├── server.py # FastMCP entry point + health endpoint
│ ├── auth.py # Bearer token verifier for remote access
│ ├── config.py # Environment configuration
│ ├── models/ # Pydantic data models
│ ├── storage/ # SQLite + encrypted credentials
│ ├── clients/ # API clients + resilience + cache
│ ├── matching/ # Cross-platform venue ID resolution
│ └── tools/ # MCP tool definitions
├── tests/ # 1193 tests, 100% branch coverage
├── data/ # Runtime: DB, logs, credentials (gitignored)
├── Dockerfile # Container build for remote hosting
├── docker-compose.yml # MCP + Cloudflare Tunnel orchestration
├── pyproject.toml
├── .env.example
└── README.mdDevelopment
# Activate virtual environment
source .venv/bin/activate
# Run full validation (lint + tests + coverage + import check)
bash scripts/validate.sh
# Run tests only
bash scripts/test.sh
# Run linter only
bash scripts/lint.shTesting: 1193 unit tests with 100% branch coverage (fail_under = 100 enforced).
Integration Tests
On-demand integration tests exercise the full stack against live Resy and OpenTable APIs:
# Run all integration tests
python -m pytest tests/integration/ -m integration -v
# Resy only
python -m pytest tests/integration/test_resy_integration.py -m integration -v
# OpenTable only (requires session cookies — see step 9 above)
python -m pytest tests/integration/test_opentable_integration.py -m integration -v
# With a specific restaurant / date
INTEGRATION_RESTAURANT="Lilia" INTEGRATION_DATE="2026-03-15" \
python -m pytest tests/integration/ -m integration -vIntegration tests are excluded from validate.sh and default pytest runs. They require real credentials in the credential store (set up via python -m src.setup).
Prior Art
Several MCP restaurant reservation servers already exist — these serve as reference implementations:
Repository | Language | What It Does |
TypeScript | Most complete — unified Resy+OpenTable search, direct Resy booking, | |
Python | Claude Desktop focused — encrypted storage, multi-account, calendar export (ICS) | |
Python | PyPI-published ( | |
TypeScript | Google Maps discovery with mood-based filtering (mock booking only) |
What we add: Full Google Places integration, weather-aware outdoor seating, dining companion dietary tracking, visit history with reviews, and true dual-platform booking (Resy + OpenTable).
Documentation
Document | Description |
Agent instructions — primary entry point for AI engineers | |
Code patterns, architecture rules, testing mandate | |
Master EPIC guide — dependency graph, tool inventory | |
High-level architecture, API landscape, data models | |
EPIC-08 resilience implementation decisions | |
Remote hosting: Docker, streamable-http, bearer auth |
Risks & Mitigations
Risk | Mitigation |
Resy API brittleness | 3-6 month breakage cycle; 3-layer fallback (API -> Playwright -> deep links) |
Resy blocks unofficial API | Low request rates; OpenTable fallback |
Resy auth tokens expire | Auto-refresh via Playwright login |
OpenTable bot detection | Realistic delays (>30s); Playwright browser automation |
Google API costs spike | In-memory LRU cache with 5-min TTL; cost tracking via |
Account deactivation | Personal accounts only; no commercial patterns |
Legal Considerations
The NY Restaurant Reservation Anti-Piracy Act (S.9365A, effective February 2025) prohibits third-party services from listing or selling restaurant reservations without written restaurant agreements. Penalties: $1,000/violation/day.
For this project: Personal-use automation is not explicitly prohibited, but both Resy's and OpenTable's Terms of Service prohibit automated access. This is a personal tool, not a commercial service.
Future Ideas
LA expansion (after NYC is stable)
Shared preferences with partner (two-user mode)
Restaurant deal tracking (NYC Restaurant Week, etc.)
Tock integration (ticketed dining experiences)
Yelp integration (official MCP server exists with booking support)
Google Calendar API (OAuth2) for automatic sync (currently URL-based)
SQLite cache tier for cross-session persistence
Status: All 8 EPICs complete. Remote hosting via Docker + Cloudflare Tunnel. 1193 tests, 100% branch coverage. Integration tests for Resy and OpenTable.
Available Tools
23 toolsapi_costsB
Show API usage costs broken down by provider.
Args: days: Number of days to look back (default 30).
Returns: Formatted table of API costs per provider.
Inputs: days. Returns: result
cancel_reservationA
Cancel an existing reservation (Resy or OpenTable).
Provide either the restaurant name (cancels most recent upcoming) or a specific confirmation ID.
Args: restaurant_name: Restaurant name to look up. confirmation_id: Specific confirmation ID.
Returns: Cancellation confirmation or error.
Inputs: restaurant_name, confirmation_id. Returns: result
check_availabilityA
Check reservation availability at a restaurant. Searches both Resy and OpenTable when available.
Args: restaurant_name: Name of the restaurant. date: Date to check — "2026-02-14", "Saturday", "tomorrow", etc. party_size: Number of diners. preferred_time: Preferred time like "19:00". Results are sorted by proximity to this time if provided.
Returns: Available time slots with platform info, or a message if none found.
Inputs: restaurant_name (required), date (required), party_size, preferred_time. Returns: result
get_my_preferencesA
Show all your current restaurant preferences including dietary restrictions, favorite cuisines, saved locations, and dining defaults.
Returns: A formatted summary of all preferences.
Inputs: no named parameters. Returns: result
get_recommendationsA
Get personalized restaurant recommendations based on your history, preferences, and current context (weather, recent visits).
Args: occasion: The type of dining occasion. Options: "date_night" - romantic, quieter spots "casual" - relaxed, neighborhood places "group_dinner" - accommodates larger parties "special" - high-end, celebration-worthy "quick" - fast, nearby options Leave empty for general recommendations. party_size: Number of diners. location: "home", "work", or an address. group: Name of a saved group — their restrictions will be applied. exclude_recent_days: Don't recommend places visited in the last N days.
Returns: Curated list of 3-5 restaurants with reasons for each recommendation.
Inputs: occasion, party_size, location, group, +1 more. Returns: result
list_groupsB
List all saved groups with their members and merged dietary restrictions.
Returns: Formatted list of groups with member details.
Inputs: no named parameters. Returns: result
list_peopleA
List all saved dining companions with their dietary restrictions and notes.
Returns: Formatted list of all people and their preferences.
Inputs: no named parameters. Returns: result
log_visitA
Log a restaurant visit (for places booked outside the system). Visits booked through this assistant are logged automatically.
Args: restaurant_name: Name of the restaurant you visited. date_str: Date of visit, e.g. "2026-02-10" or "last Tuesday" (default: today). party_size: Number of diners. companions: Names of who you dined with, e.g. ["Alice", "Bob"]. cuisine: Type of cuisine, e.g. "italian", "mexican".
Returns: Confirmation with visit ID for adding a review.
Inputs: restaurant_name (required), date_str, party_size, companions, +1 more. Returns: result
make_reservationA
Book a reservation at a restaurant via Resy or OpenTable. Only call this after the user has confirmed they want to book.
Args: restaurant_name: Name of the restaurant. date: Reservation date — "2026-02-14", "Saturday", etc. time: Reservation time — "19:00" or "7:00 PM". party_size: Number of diners. special_requests: E.g. "birthday", "quiet table".
Returns: Confirmation with details and confirmation number.
Inputs: restaurant_name (required), date (required), time (required), party_size, +1 more. Returns: result
manage_blacklistA
Add or remove restaurants from your blacklist. Blacklisted restaurants will never appear in search results or recommendations.
Args: restaurant_name: Name of the restaurant. action: "add" to blacklist, "remove" to un-blacklist. reason: Why you're blacklisting (for your records).
Returns: Confirmation of the action.
Inputs: restaurant_name (required), action, reason. Returns: result
manage_groupA
Create, update, or remove a named group of dining companions.
Args: group_name: Name for the group, e.g. "work_team", "family". action: "add" to create/update, "remove" to delete the group. members: List of people names (must already be saved via manage_person).
Returns: Confirmation with group details and merged dietary restrictions.
Inputs: group_name (required), action, members. Returns: result
manage_personA
Add, update, or remove a dining companion.
Args: name: Person's name (case-insensitive matching). action: "add" to create/update, "remove" to delete. dietary_restrictions: Their restrictions, e.g. ["nut_allergy", "vegan"]. no_alcohol: True if they don't drink alcohol. notes: Any other notes, e.g. "Prefers window seats".
Returns: Confirmation of the action taken.
Inputs: name (required), action, dietary_restrictions, no_alcohol, +1 more. Returns: result
manage_wishlistA
Add or remove restaurants from your wishlist — places you want to try in the future. Wishlisted restaurants get a boost in recommendations when you're nearby.
The restaurant must already appear in search results (search first).
Args: restaurant_name: Name of the restaurant. action: "add" to wishlist, "remove" to un-wishlist. notes: Free-text notes (e.g. "get the tasting menu"). tags: Comma-separated tags for filtering, e.g. "date night, special occasion". Common tags: date night, group dinner, special occasion, brunch, solo, outdoor, weeknight.
Returns: Confirmation of the action.
Inputs: restaurant_name (required), action, notes, tags. Returns: result
my_reservationsA
Show all your upcoming reservations across Resy and OpenTable.
Returns: Formatted list of upcoming reservations with dates, times, party sizes, and confirmation numbers.
Inputs: no named parameters. Returns: result
my_wishlistA
Show your restaurant wishlist, optionally filtered by tag.
Args: tag: Filter by a single tag (e.g. "date night").
Returns: Numbered list of wishlisted restaurants with details.
Inputs: tag. Returns: result
rate_visitA
Rate a restaurant you recently visited. Used to improve future recommendations.
Args: restaurant_name: Name of the restaurant. would_return: True if you'd go back, False if not. overall_rating: 1-5 stars (optional). noise_level: "quiet", "moderate", or "loud" — helps calibrate future recs. dishes: List of dishes with ratings, e.g. [{"name": "cacio e pepe", "rating": 5, "order_again": true}]. notes: Any additional notes, e.g. "Great for date night".
Returns: Confirmation that the review was saved.
Inputs: restaurant_name (required), would_return (required), overall_rating, noise_level, +2 more. Returns: result
search_for_groupA
Search for restaurants suitable for a saved group. Automatically merges all members' dietary restrictions and finds restaurants that work for everyone.
Args: group_name: Name of the saved group (e.g., "work_team", "family"). location: Where to search near. date: Date for the dinner. time: Preferred time. cuisine: Specific cuisine (optional).
Returns: Restaurant recommendations with notes on dietary compatibility.
Inputs: group_name (required), location, date, time, +1 more. Returns: result
search_restaurantsA
Search for restaurants matching your criteria near a location. Automatically applies your dietary restrictions, cuisine preferences, minimum rating threshold, and blacklist.
Args: cuisine: Type of food, e.g. "italian", "mexican", "sushi". Leave empty to search all cuisines. location: Where to search near. Use "home", "work", or a specific NYC address. party_size: Number of diners. price_max: Maximum price level 1-4. Leave empty to use your saved price preferences. outdoor_seating: True if outdoor seating is specifically desired. query: Free-text search for specific restaurants or features, e.g. "rooftop bar", "Carbone". max_results: Maximum restaurants to return (default 5, max 10).
Returns: Formatted list of matching restaurants with ratings, prices, walking distance, and cuisine info.
Inputs: cuisine, location, party_size, price_max, +3 more. Returns: result
setup_preferencesB
Set up or update your restaurant preferences. Call this when the user first configures the assistant or wants to change their profile.
Args: name: User's first name. home_address: Home address for "near home" searches. work_address: Work address for "near work" searches. dietary_restrictions: E.g. ["vegetarian", "nut_allergy"]. favorite_cuisines: Cuisines you love, e.g. ["italian", "korean"]. cuisines_to_avoid: Cuisines you dislike, e.g. ["fast_food"]. price_levels: Acceptable price levels 1-4, e.g. [2, 3]. noise_preference: "quiet", "moderate", or "lively". seating_preference: "indoor", "outdoor", or "no_preference". max_walk_minutes: Maximum walking time from location (default 15). default_party_size: Usual party size (default 2). rating_threshold: Minimum Google rating to show (default 4.0).
Returns: Confirmation message with saved preferences summary.
Inputs: name (required), home_address, work_address, dietary_restrictions, +8 more. Returns: result
store_opentable_credentialsA
Save your OpenTable DAPI credentials for automated booking.
The CSRF token (x-csrf-token) is required for booking. To get it:
Log into opentable.com in your browser
Open DevTools → Network tab
Make any action (search, etc.)
Find any request to
/dapi/and copy thex-csrf-tokenheader value
Recommended: Set OPENTABLE_CSRF_TOKEN as an environment variable so it never appears in chat history.
Args: csrf_token: The x-csrf-token value from your browser session (or set OPENTABLE_CSRF_TOKEN env var). email: Your OpenTable account email (or set OPENTABLE_EMAIL env var). first_name: First name for reservations. last_name: Last name for reservations. phone: Phone number for reservations.
Returns: Confirmation that credentials were saved.
Inputs: csrf_token, email, first_name, last_name, +1 more. Returns: result
store_resy_credentialsA
Save your Resy account credentials for automated booking.
Recommended: Set RESY_EMAIL and RESY_PASSWORD as environment variables (or in your .env file) so credentials never appear in chat history. If env vars are set, call this tool with no arguments.
Credentials are encrypted and stored locally — never sent anywhere except to Resy's own servers for authentication. The password is NOT persisted after authentication.
Args: email: Your Resy account email (or set RESY_EMAIL env var). password: Your Resy account password (or set RESY_PASSWORD env var).
Returns: Confirmation that credentials were saved and verified, or an error if login failed.
Inputs: email, password. Returns: result
update_preferencesA
Update specific preferences without resetting everything. Only provided fields are changed; everything else stays the same.
Args: dietary_restrictions: Replace full dietary list. add_favorite_cuisine: Add a single cuisine to favorites. remove_favorite_cuisine: Remove a cuisine from favorites. add_avoid_cuisine: Add a cuisine to avoid list. noise_preference: "quiet", "moderate", or "lively". seating_preference: "indoor", "outdoor", or "no_preference". rating_threshold: Minimum Google rating. default_party_size: Usual party size. max_walk_minutes: Maximum walking time.
Returns: Confirmation of what was changed.
Inputs: dietary_restrictions, add_favorite_cuisine, remove_favorite_cuisine, add_avoid_cuisine, +5 more. Returns: result
visit_historyB
Show your recent restaurant visit history.
Args: days: How many days back to look (default 90). cuisine: Filter by cuisine type (optional).
Returns: Formatted list of recent visits with dates, ratings, and notes.
Inputs: days, cuisine. Returns: result
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
Loading tool schema changelog…
TDQS
Scored across 23 tools
Most tools have distinct purposes, but some overlap exists. For example, 'search_restaurants' and 'get_recommendations' both provide restaurant suggestions, though the latter is personalized and contextual. Similarly, 'check_availability' and 'search_for_group' both find available restaurants, but the latter focuses on group compatibility. Descriptions help clarify these distinctions, but an agent might occasionally misselect between them.
Tool names follow a consistent snake_case pattern with clear verb_noun structures. Examples include 'check_availability', 'manage_blacklist', 'store_resy_credentials', and 'update_preferences'. There are no deviations in naming style, making the set predictable and easy to understand.
With 23 tools, the count is on the higher side but reasonable for the comprehensive dining assistant domain. It covers reservations, preferences, groups, history, and credentials management. While slightly heavy, each tool appears to serve a specific function without obvious redundancy, justifying its inclusion.
The toolset provides complete coverage for a dining assistant, including CRUD operations for reservations, preferences, groups, and wishlists. It handles search, recommendations, availability checks, visit logging, rating, and credential management. There are no apparent gaps; agents can perform end-to-end workflows from setup to booking to review.
Maintenance
Related MCP Connectors
OpenTable MCP server for ChatGPT, Claude and any AI agent: search restaurants with open tables, check availability, book a reservation, modify a reservation, cancel a reservation, list your reservations, read the reservation book and add guest notes and tags in your own OpenTable account. Cotlet works in the account through your Cloud Browser, a real Chrome where you sign in to OpenTable yourself; Cotlet does not store your password and you need no OpenTable API key. About the official OpenTable API: OpenTable has APIs only for approved partners (checked 27 Oct 2026). Booking, Sync, CRM, Directory and Reviews APIs need an application through the OpenTable partner program, and they work per restaurant, not on a diner account. Setup and FAQ: https://cotlet.dev/integrations/opentable-api-mcp-server
Resy MCP server for ChatGPT, Claude and any AI agent: search restaurants, check availability, book a reservation, list reservations, change a reservation, cancel a reservation, set a Notify alert and remove a Notify alert in your own Resy account. Cotlet works in the account through your Cloud Browser, a real Chrome where you sign in to Resy yourself; Cotlet does not store your password and you need no Resy API key. About the official Resy API: Resy has no public API (checked 27 Oct 2026). Its official Resy app runs only inside Claude and ChatGPT: in Claude it shows live availability and sends you to Resy to book, in ChatGPT it can also book a table. Setup and FAQ: https://cotlet.dev/integrations/resy-api-mcp-server
Hosted MCP server to manage a restaurant menu from AI agents - 39 tools over the DuckHub API.
MCP server unifying ERPs, CRMs, APIs and knowledge base for Claude, ChatGPT and Gemini.
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