OpenAPI 사양을 AI-powered 증강 및 검증을 가진 생산-ready Model Context Protocol (MCP) 서버로 변환

OpenAPI to MCP는 OpenAPI/Swagger 사양을 완전 기능 MCP 서버로 자동 변환하는 강력한 도구입니다. 큰 언어 모델(LLMs)을 활용하여 기존 API 문서에서 MCP 호환 서버 구현을 분석, 향상 및 생성합니다.

[!주의사항] 이 저장소에 제공된 예는 실험 및 교육 목적으로만 사용됩니다. 그들은 개념과 기술을 보여주고 그러나 생산 환경에서 직접적인 사용을 위해 예정되지 않습니다. Amazon Bedrock Guardrails가 신속한 주입에 대해 보호하기 위해 확인하십시오.

내용 표

• 새로운 것
• 왜 이 해결책
• 건축
• 특징
• 필수
• 설치
• 사용
• 기본 사용
• 명령줄 옵션
• 환경변수
• 예제
• 구성
• 산출 구조
• AI 공급자
• 기여
• 보안
• 라이센스

새로운 것

##의 v0.1.0

• 핵심 기능을 가진 처음 방출
• OpenAPI 3.0 및 Swagger 2.0 사양 지원
• AI-powered 평가 및 향상

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