A FastAPI-based implementation of the Model Context Protocol that enables standardized interaction between AI models and development environments, making it easier for developers to integrate and manage AI tasks.
A Model Context Protocol (MCP) server implementation built with Python and FastAPI for educational purposes. Demonstrates MCP server functionality through a books API interface.
An end-to-end test MCP server built with FastMCP that exposes a REST API as a set of tools for AI agents. It enables LLMs to perform CRUD operations on an upstream API by mapping HTTP methods to MCP tools.
Enables MCP clients to connect to LLM/API services using the Model Context Protocol, providing real-time interaction and tool access. Also offers RESTful API endpoints via FastAPI for programmatic integration with existing systems.
MCP server that automates the generation of production-grade async FastAPI services following Clean Architecture, with self-healing pytest-based verification and OpenAPI spec ingestion for rapid CRUD API development.
