"A server for fetching restaurant recommendations and saving them to Notepad" matching MCP servers:

Enables AI agents to get book recommendations by topic or mood, discover serendipitous blind-date picks from first sentences, and access free public-domain classics with read-now availability, all with no API keys required.

A permission-aware MCP server for restaurant backend operations, enabling role-based access to menus, orders, sales, and drafting specials with a human approval workflow.

MCP server exposing restaurant tools including search by cuisine, price, rating, and open status, restaurant details, user preferences stored in Neon Postgres, and RAG knowledge search covering 50 mock restaurants across Scottsdale and Phoenix.

MCP server that enables AI models to open the Windows Notepad application via function calling, demonstrating the complete MCP tool call workflow.

MCP server for Spice Garden demo restaurant that enables querying orders, inventory, tables, and sales data from a SQLite database, with an optional OpenRouter-powered chat assistant.

Enables customers and AI agents to check restaurant table availability and create bookings, returning confirmations or nearby alternative times when a slot is full, with atomic capacity control and idempotent handling.

Enables AI agents to discover long-tail podcasts through randomized probe queries, trend lookups, and RSS feed peeks, providing cadence, episode length, and feed details without requiring API keys.

Enables users to search, check availability, and book restaurant reservations across Resy and OpenTable platforms. It supports direct booking for Resy and includes an automated reservation 'sniper' for securing high-demand slots the moment they become available.

Enables AI agents to query a restaurant's governed knowledge documents, including menu, allergen info, reservation and refund policies, and hours, over MCP.

Enables simulating customer orders from a dummy restaurant menu and tracking their status in real-time via RESTful APIs.

A fault-tolerant, stealth-enabled Model Context Protocol (MCP) server for web searching and content fetching. Built for AI Agents (Cursor, Claude Code, OpenCode), it uses a stealth browser engine to fetch pages, dynamically handles SPAs/React, and converts bloat into token-optimized Markdown.

MCP server for Expedia travel recommendations. Enables LLMs to search hotels, flights, activities, and car rentals using natural language.

Provides hospitality intelligence tools for restaurant optimization, including menu profitability analysis, food cost calculation, reservation management, review sentiment analysis, and allergen checking.

Enables MCP-compatible AI assistants to analyze restaurant menu economics from CSV data, including food-cost percentages, contribution margins, gross sales, ingredient costs, and item rankings. It can flag loss-making items and periods with no sales.

Enables AI agents and clients to check table availability and book restaurant reservations for a given date, time, and party size, returning either a confirmed booking or the nearest alternative slots. It also supports idempotent retries so replayed requests return the original reservation rather than double-booking a table.

MCP server for NYC Restaurant Week, enabling search and lookup of participating restaurants, date checks, and filter options.

Enables agents to discover and invoke travel recommendations with partner-specific caps and exclusions via MCP JSON-RPC.

MCP server for agentic travel recommendations, exposing tools to retrieve member profiles and personalized travel recommendations with partner-specific rules and policies.

Enables an AI agent to fetch member profiles and generate deterministic, partner-rule-compliant travel recommendations on a multi-tenant white-label platform.

Enables building and evaluating LangGraph agents over a protocol-level MCP server to measure failure rates across four agent topologies.