将您的OpenAPI规范转化为生产准备的模型上下文协议（MCP）服务器，并配备AI驱动的增强和验证

OpenAPI 到 MCP 是一个强大的工具，可以自动将 OpenAPI/Swagger 规范转换为功能完整的 MCP 服务器。它利用大型语言模型（LLM）分析、增强并从现有 API 文档生成兼容 MCP 的服务器实现。

[!注意] 本仓库中提供的示例仅供实验和教育用途。它们展示了概念和技术，但不适用于生产环境中的直接使用。确保安装亚马逊基岩护栏以防止紧急注入。

目录

• 最新动态
• 为什么要选这种方案
• 建筑学
• 特色
• 先决条件
• 安装
• 使用情况
• 基本用法
• 命令行选项
• 环境变量
• 示例
• 配置
• 输出结构
• 人工智能提供商
• 贡献
• 安全
• 许可证

最新动态

v0.1.0

• 初始版本，具备核心功能
• 支持 OpenAPI 3.0 和 Swagger 2.0 规范
• 人工智能驱动的评估与增强

OpenAPI to MCP Converter

Transform your OpenAPI specifications into production-ready Model Context Protocol (MCP) servers with AI-powered enhancement and validation

OpenAPI to MCP is a powerful tool that automatically converts OpenAPI/Swagger specifications into fully functional MCP servers. It leverages Large Language Models (LLMs) to analyze, enhance, and generate MCP-compatible server implementations from your existing API documentation.

[!CAUTION] The examples provided in this repository are for experimental and educational purposes only. They demonstrate concepts and techniques but are not intended for direct use in production environments. Make sure to have Amazon Bedrock Guardrails in place to protect against prompt injection.

Table of Contents

• What’s New
• Why This Solution
• Architecture
• Features
• Prerequisites
• Installation
• Usage
  • Basic Usage
  • Command Line Options
  • Environment Variables
• Examples
• Configuration
• Output Structure
• AI Providers
• Contributing
• Security
• License

What’s New

v0.1.0

• Initial release with core functionality
• Support for OpenAPI 3.0 and Swagger 2.0 specifications
• AI-powered evaluation and enhancement
• Automatic MCP server and client generation
• Support for Amazon Bedrock and Anthropic Claude
• Comprehensive evaluation reports with usage tracking

Why This Solution

As enterprises transition towards being AI-ready, a critical piece of the puzzle is ensuring their APIs are AI-ready as well. API specifications that were written for human consumption often lack the level of detail that Large Language Models require for reliable operation. While human developers can rely on domain expertise and tribal knowledge to fill in documentation gaps, LLMs may hallucinate missing information, leading to AI agents that cannot reliably invoke the right tools at the right time.

Common issues in human-oriented API documentation that impact AI readiness include:

• Missing or vague descriptions: Endpoints and parameters that lack detailed explanations of their purpose and behavior
• Undefined parameter constraints: Parameters without clearly defined ranges, formats, or validation rules