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
    └── doupocangqiong.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

Create a .env file in the root directory with the following variables:

API_KEY=your_llm_api_key
BASE_URL=https://dashscope.aliyuncs.com/compatible-mode/v1
MODEL=qwen-plus
EMBED_MODEL=sentence-transformers/all-MiniLM-L6-v2

Dependencies

pip install mcp langchain langchain-community langchain-openai chromadb httpx python-dotenv openai

Usage

Running the Weather Agent

To start the client and connect it to the weather server:

python client.py server.py

Running the RAG Agent

To initialize the knowledge base and start the RAG-enabled agent:

python rag_agent.py --server_script rag_server.py

Protocol Implementation Details

The implementation strictly adheres to the MCP specification:

1. Initialization: Client initializes the session and retrieves tool manifests.
2. Tool Discovery: LLM is informed of available functions via JSON schema.
3. Execution: Client intercepts tool_calls, executes the corresponding server function, and returns the result to the LLM for final synthesis.