"FastAPI" matching MCP servers:

A zero-configuration tool that automatically converts FastAPI endpoints into Model Context Protocol (MCP) tools, enabling AI systems to interact with your API through natural language.

Exposes FastAPI endpoints as Model Context Protocol (MCP) tools while preserving existing authentication, schemas, and documentation. It enables seamless integration of FastAPI services into MCP ecosystems using a native ASGI transport layer.

A zero-configuration tool that automatically exposes FastAPI endpoints as Model Context Protocol (MCP) tools, allowing LLM systems like Claude to interact with your API without additional coding.

Production-ready API server template integrating FastAPI with Model Context Protocol (MCP) for LLM integration, structured logging, and comprehensive testing.

Mounts an MCP server onto FastAPI to let AI agents inspect routes, endpoints, and schemas at runtime.

A FastAPI library that provides Model Context Protocol tools for endpoint introspection and OpenAPI documentation, allowing AI agents to discover and understand API endpoints.

A Model Context Protocol server that provides tools for introspecting and analyzing FastAPI applications, including route discovery, model schema extraction, and source code viewing. It enables users to explore API structures, generate documentation, and debug dependency injection hierarchies through natural language.

A REST API built with FastAPI that exposes endpoints via Model Context Protocol (MCP), allowing clients to interact with CRUD operations through MCP interfaces.

Instantly converts any running FastAPI application into an MCP server by parsing its OpenAPI spec, enabling Claude to call all endpoints via natural language without manual tool writing.

Wraps FastAPI REST endpoints as MCP tools, enabling natural language interaction with user management, task management, and mathematical calculations through Gemini CLI.

Exposes FastAPI routes as MCP tools with auto-discovery, Pydantic schema extraction, and built-in observability.

Converts FastAPI application OpenAPI documentation into MCP tools for AI assistants to efficiently query API information. Reduces token consumption by enabling on-demand, structured API queries instead of loading complete OpenAPI specifications.

Enables Gemini AI to interact with a FastAPI application through MCP tools for user management, task management, and dice rolling functionality. Provides natural language access to REST API endpoints including CRUD operations, health checks, and application statistics.

Enables natural language interaction with FastAPI applications through Google's Gemini AI using MCP tools. Provides CRUD operations for user management and application health monitoring through conversational prompts.

Probes your live API and classifies why each endpoint failed (root cause, evidence, and a calibrated confidence level), exposed over MCP so your AI assistant debugs from evidence instead of guessing. Works with FastAPI, Express, Next.js, tRPC, and GraphQL.

Converts REST API code (like NestJS controllers or FastAPI endpoints) to Postman collections and environments, helping developers automatically sync their API endpoints with Postman.

Exposes FastAPI API-key management and request-making routes as MCP tools, enabling users to manage and interact with their APIs via Claude.

A FastAPI-based Model Context Protocol server that exposes herd data through a discoverable API, with local and containerized deployment options.

Analyzes API codebases from GitHub and Bitbucket repositories to generate Postman collections, business reports, and detailed code insights. Supports multiple frameworks including FastAPI, Spring Boot, Flask, Express, and OpenAPI/Swagger specifications.

A FastAPI + FastMCP dashboard for registering MCP mock server contracts and connecting to them via Streamable HTTP endpoints.