OpenAPI to MCP Server

MCP Server

Convert OpenAPI specs into FastMCP servers

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Updated Jun 3, 2025

About

OpenAPI2Mcp is a command‑line tool that parses OpenAPI 3.x specifications and generates FastMCP server code, supporting multiple transports (stdio, Google Pub/Sub) and optional LLM descriptions.

Capabilities

Resources

Access data sources

Tools

Execute functions

Prompts

Pre-built templates

Sampling

AI model interactions

OpenAPI to MCP conversion workflow

Overview

The openapi2mcp tool bridges the gap between standard API definitions and the Model Context Protocol (MCP), enabling developers to expose existing OpenAPI 3.x services as fully‑featured MCP servers with minimal effort. By parsing a JSON or YAML specification, it automatically generates Python code that implements the required MCP endpoints—resources, tools, prompts, and sampling logic—so an AI assistant can discover and invoke the API’s capabilities as if they were native language constructs. This eliminates manual boilerplate, reduces errors, and guarantees that the generated server adheres to MCP’s strict interface contracts.

For developers building AI‑powered applications, having an MCP server that mirrors a legacy REST API is invaluable. It allows language models to understand and interact with the API through natural language, turning arbitrary endpoints into callable tools. The generated file supplies the model with concise, human‑readable descriptions of each tool and resource, ensuring smooth prompt engineering and accurate intent recognition. The result is a seamless integration where an AI assistant can, for example, retrieve user data or trigger workflows without any custom glue code.

Key features include:

Transport flexibility: choose between or , covering local, cloud‑native, and streaming scenarios.
Mount path configuration: prepend a custom base URL (, ) so the generated server can coexist with other services.
Automated validation: the command scans the produced Python file for syntax correctness and MCP pattern compliance, catching issues early in the CI pipeline.
Extensible output: developers can add or modify generated modules to incorporate custom business logic while preserving the MCP contract.

Typical use cases span from rapid prototyping of AI assistants that need to call internal microservices, to production deployments where a legacy API must be exposed through an MCP gateway for multimodal agents. By converting OpenAPI specs to MCP servers, teams can leverage their existing API documentation and tooling ecosystem while unlocking advanced conversational capabilities.