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LangGraph MCP Server

LangGraph MCP Server

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github.com/rezawrAI & Agents1Updated 75w ago

About LangGraph MCP Server

LangGraph MCP Server is a modular Python MCP server for LangGraph documentation. Its separate tool and resource components make it suitable for extending and maintaining documentation access.

Key features

  1. 01Modular server architecture
  2. 02Separate tool and resource registration
  3. 03Centralized configuration
  4. 04Extensible component structure

Use cases

  1. 01Expose LangGraph documentation to MCP clients
  2. 02Add custom documentation tools
  3. 03Register documentation resources separately

MCP support

It exposes tools and resources for MCP clients. The server registers these through its main entry point.

Configuration

Local server config is the same for every client. Copy it into your client’s MCP config file.

No install method listed yet

We haven’t collected a package or remote endpoint for this server. Follow the instructions in the project README.

Repository

Tools

No tool list yet

Local servers expose tools only once running on your machine, and the maintainer has not provided a tool list.

Details

From the project README

MseeP.ai Security Assessment Badge

LangGraph MCP Server

A clean, modular implementation of a Model Context Protocol (MCP) server for LangGraph documentation.

Architecture

This project follows a clean architecture pattern to make the MCP server more maintainable and easier to debug as more functionality is added.

Directory Structure
app/
├── config.py                  # Configuration settings
├── server.py                  # Main server entry point
├── resources/                 # Resources that can be accessed by clients
│   ├── __init__.py            # Resource registration
│   └── langgraph_resources.py # LangGraph-specific resources
├── tools/                     # Tools that can be called by clients
│   ├── __init__.py            # Tool registration
│   └── langgraph_tools.py     # LangGraph-specific tools
└── utils/                     # Utility functions
    ├── __init__.py
    └── logging_utils.py       # Logging utilities

Core Components

  1. Server: The main entry point that initializes the MCP server and registers all tools and resources.
  2. Config: Central location for all configuration settings.
  3. Tools: Functions that can be called by clients to perform specific tasks.
  4. Resources: Data sources that can be accessed by clients.
  5. Utils: Utility functions used throughout the application.

Adding New Functionality

Adding a New Tool
  1. Create a new file in the app/tools/ directory (e.g., weather_tools.py).
  2. Define your tool functions in this file.
  3. Create a registration function (e.g., register_weather_tools).
  4. Import and call this registration function in app/tools/__init__.py.

Example:

# app/tools/weather_tools.py
def register_weather_tools(mcp):
    mcp.tool()(get_weather)

def get_weather(city: str):
    """Get weather for a city"""
    # Implementation
    return f"Weather for {city}: Sunny, 75°F"

# app/tools/__init__.py
from app.tools.langgraph_tools import register_langgraph_tools
from app.tools.weather_tools import register_weather_tools

def register_tools(mcp):
    register_langgraph_tools(mcp)
    register_weather_tools(mcp)
Adding a New Resource
  1. Create a new file in the app/resources/ directory (e.g., weather_resources.py).
  2. Define your resource functions in this file.
  3. Create a registration function (e.g., register_weather_resources).
  4. Import and call this registration function in app/resources/__init__.py.

Example:

# app/resources/weather_resources.py
def register_weather_resources(mcp):
    mcp.resource("weather://forecast")(get_weather_forecast)

def get_weather_forecast():
    """Get weather forecast"""
    # Implementation
    return "5-day weather forecast data"

# app/resources/__init__.py
from app.resources.langgraph_resources import register_langgraph_resources
from app.resources.weather_resources import register_weather_resources

def register_resources(mcp):
    register_langgraph_resources(mcp)
    register_weather_resources(mcp)

Running the Server

To run the server:

python -m app.server

Benefits of This Architecture

  1. Modularity: Each component has a single responsibility.
  2. Extensibility: Easy to add new tools and resources without modifying existing code.
  3. Maintainability: Organized structure makes debugging easier.
  4. Scalability: Can handle growth as more functionality is added.
  5. Testability: Components can be tested in isolation.

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