** 멀티 시약 AI 시스템 개발 및 최적화를 위한 MCP 툴링 **

=============================================== Model Context Protocol (MCP)과 협력하여 AI 에이전트와 다양한 데이터 소스, API 및 서비스 간의 원활한 통합을 제공합니다. MCP Kit는 다중 시약 시스템 구축, 테스트, 배포 여부를 간단히 합니다.

특징

# # ** 유연한 대상 시스템 **

• MCP Servers: 기존 MCP 서버에 연결하기 (참여 또는 사양)
• OpenAPI 통합: OpenAPI/Swagger specs를 사용하여 REST APIs를 MCP 툴로 자동 변환
• ** 충격 응답 **: LLM 또는 임의 발전기를 사용하여 실제 테스트 데이터를 생성
• Multiplexing: 통합된 인터페이스로 여러 대상을 결합

**Framework 어댑터 **

• OpenAI Agents SDK: OpenAI의 에이전트 프레임 워크와 기본 통합
• LangGraph: LangGraph 워크플로우에 대한 원활한 도구 통합
• Generic Client Sessions: MCP 프로토콜 통신
• ** 공식 MCP Server**: 표준 MCP 서버 래퍼

**Configuration-Driven 아키텍처 **

MCP Kit Python

MCP tooling for developing and optimizing multi-agent AI systems

A comprehensive toolkit for working with the Model Context Protocol (MCP), providing seamless integration between AI agents and various data sources, APIs, and services. Whether you’re building, testing, or deploying multi-agent systems, MCP Kit simplifies the complexity of tool orchestration and provides powerful mocking capabilities for development.

Features

Flexible Target System

• MCP Servers: Connect to existing MCP servers (hosted or from specifications)
• OpenAPI Integration: Automatically convert REST APIs to MCP tools using OpenAPI/Swagger specs
• Mock Responses: Generate realistic test data using LLM or random generators
• Multiplexing: Combine multiple targets into a unified interface

Framework Adapters

• OpenAI Agents SDK: Native integration with OpenAI’s agent framework
• LangGraph: Seamless tool integration for LangGraph workflows
• Generic Client Sessions: Direct MCP protocol communication
• Official MCP Server: Standard MCP server wrapper

Configuration-Driven Architecture

• YAML/JSON Configuration: Declarative setup for complex workflows
• Factory Pattern: Clean, testable component creation
• Environment Variables: Secure credential management

Advanced Response Generation

• LLM-Powered Mocking: Generate contextually appropriate responses using LLMs
• Random Data Generation: Create test data for development and testing
• Custom Generators: Implement your own response generation logic

---

Quick Start

Installation

uv add mcp-kit

Basic Usage

First you write the Proxy config:

# proxy_config.yaml
""" A mocked REST API target given the OpenAPI spec using LLM-generated responses
"""
target:
  type: mocked
  base_target:
    type: oas
    name: base-oas-server
    spec_url: https://petstore3.swagger.io/api/v3/openapi.json
  tool_response_generator:
    type: llm
    model: openai/gpt-4.1-nano

Don’t forget to setup the LLM API KEY:

# .env
OPENAI_API_KEY="your_openai_key"

Then we can use it as any other MCP:

# main.py
from mcp_kit import ProxyMCP

async def main():
    # Create proxy from configuration
    proxy = ProxyMCP.from_config("proxy_config.yaml")

    # Use with MCP client session adapter
    async with proxy.client_session_adapter() as session:
        tools = await session.list_tools()
        result = await session.call_tool("get_pet", {"pet_id": "777"})
        print(result)

if __name__ == "__main__":
    import asyncio