MCP Agent Orchestrator는 ** Model Context Protocol (MCP)**의 프로급 Python 구현입니다. 그것은 표준화 된 통신 층을 통해 외부 도구와 지식베이스와 상호 작용하는 대형 언어 모델 (LLMs)에 대한 구조화 된 환경을 제공합니다. 프로젝트는 FastMCP] 서버 측 도구 정의 및 OpenAI 호환 인터페이스에 비동기 클라이언트 측면 브리지를 사용합니다.

시스템 아키텍처

프로젝트는 decoupled 클라이언트 서버 아키텍처를 따릅니다:

1. MCP Client: 오케스트라로서의 행위. MCP 서버의 수명주기를 관리하고 도구 발견을 수행하고 LLM 완성을 처리하고 모델을 반환하는 도구 통화를 실행합니다.
2. ** MCP Servers**: Model Context Protocol을 통해 클라이언트에 특정 함수를 노출하는 독립 서비스(Weather, RAG).
3. Transport Layer: 고성능, 로컬 간 처리 통신을 위한 표준 입력/출력(StdIO)를 사용합니다.

핵심 성분

##1. 지적인 클라이언트 교량

클라이언트 구현 (rag_agent.py, client.py]]) 용이성:

• AsyncExitStack를 이용한 비동기 수명주기 관리.
• OpenAI 호환 기능 호출을위한 자동 도구 스키마 변환.

MCP Agent Orchestrator

The MCP Agent Orchestrator is a professional-grade Python implementation of the Model Context Protocol (MCP). It provides a structured environment for Large Language Models (LLMs) to interact with external tools and knowledge bases through a standardized communication layer. The project utilizes FastMCP for server-side tool definitions and an asynchronous client-side bridge to OpenAI-compatible interfaces.

System Architecture

The project follows a decoupled client-server architecture:

1. MCP Client: Acts as the orchestrator. It manages the lifecycle of the MCP server, performs tool discovery, handles LLM completions, and executes tool calls returned by the model.
2. MCP Servers: Independent services (Weather, RAG) that expose specific functions to the client via the Model Context Protocol.
3. Transport Layer: Uses Standard Input/Output (StdIO) for high-performance, local inter-process communication.

Core Components

1. Intelligent Client Bridge

The client implementation (rag_agent.py, client.py) facilitates:

• Asynchronous lifecycle management using AsyncExitStack.
• Automatic tool schema conversion for OpenAI-compatible function calling.
• Persistent conversation state and multi-turn reasoning loops.

2. Weather Service Server

The weather server (server.py) demonstrates real-time API integration:

• Integration with external REST APIs (WeatherAPI).
• Data normalization and formatting for LLM consumption.
• Asynchronous request handling using httpx.

3. RAG Knowledge Server

The RAG server (rag_server.py) provides advanced document intelligence:

• Data Ingestion: Support for PDF and TXT formats using LangChain.
• Vector Database: Persistent storage via ChromaDB.
• Search Optimization: Implements Maximal Marginal Relevance (MMR) for diverse information retrieval.
• Embeddings: Integration with HuggingFace transformer models.

Project Structure

├── client.py             # Standard MCP client implementation
├── rag_agent.py          # Specialized agent for RAG operations
├── server.py             # Weather service MCP server
├── rag_server.py         # RAG knowledge base MCP server
├── test.py               # Connectivity test for LLM API
├── .env                  # Environment configuration
└── data/
    ├── rag_db/           # Vector store persistence directory
    └── text.txt # Sample knowledge base source

Technical Stack

• Protocol: Model Context Protocol (MCP)
• LLM Interface: OpenAI SDK (Compatible with Qwen/DashScope)
• RAG Framework: LangChain
• Vector Store: ChromaDB
• Communication: Asynchronous I/O (asyncio)

Installation

Prerequisites

• Python 3.10+
• Virtual environment (recommended)

Environment Setup