iflow-mcp_kenneth-liao-mcp-intro 0.1.0

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MCP-Langgraph Integration Tutorial

This tutorial demonstrates how to integrate Model Context Protocol (MCP) servers with Langgraph agents to create powerful, tool-enabled AI applications. The project showcases a data science assistant named Scout that can help users manage their data science projects using various MCP-powered tools.

Overview

The project implements a conversational AI agent that:

• Uses GPT-4.1 as the base model
• Integrates with multiple MCP servers for different functionalities
• Uses Langgraph for orchestrating the conversation flow
• Provides a streaming interface for real-time responses

Prerequisites

• Python 3.13+
• Node.js (for filesystem MCP server)
• Docker (for GitHub MCP server)
• UV package manager
• OpenAI API key

Project Structure

scout/
├── graph.py           # Langgraph agent implementation
├── client.py          # MCP client and streaming interface
├── client_utils.py    # Utility functions
├── main.py           # Entry point
└── my_mcp/           # MCP server configurations
    ├── config.py     # Config loading and env var resolution
    ├── mcp_config.json # MCP server definitions
    └── local_servers/ # Custom MCP server implementations

Setup

1. Clone the repository:

git clone <repository-url>
cd mcp-intro

2. Create and activate a virtual environment:

python -m venv .venv
source .venv/bin/activate  # On Windows: .venv\Scripts\activate

3. Install dependencies:

uv pip install -e .

4. Set up environment variables: Create a .env file with:

OPENAI_API_KEY=your_openai_api_key
MCP_FILESYSTEM_DIR=/path/to/projects/directory
MCP_GITHUB_PAT=your_github_personal_access_token

MCP Servers

This project integrates with four MCP servers:

1. Dataflow Server: Custom implementation for data loading and querying
2. Filesystem Server: Uses @modelcontextprotocol/server-filesystem for file operations
3. Git Server: Uses mcp-server-git for local git operations
4. GitHub Server: Uses the official GitHub MCP server for GitHub operations

Usage

1. Start the application:

python -m scout.client

2. Interact with Scout by typing your questions or requests. For example:

USER: Can you help me set up a new data science project?

3. Scout will use its tools to:

• Create and manage project directories
• Handle data loading and transformation
• Manage version control
• Interact with GitHub repositories

4. Type 'quit' or 'exit' to end the session.

How It Works

1. The graph.py file defines the Langgraph agent structure:

• Sets up the system prompt and agent state
• Configures the LLM (GPT-4)
• Defines the conversation flow graph

2. The client.py file:

• Initializes the MCP client with multiple servers
• Handles streaming responses
• Manages the interactive session

3. MCP servers provide tools for:

• File system operations
• Data manipulation
• Git operations
• GitHub interactions

Extending the Project

You can extend this project by:

1. Adding new MCP servers in my_mcp/local_servers/
2. Modifying the system prompt in graph.py
3. Adding new tools to the agent
4. Customizing the conversation flow

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.