
LangGraph MCP Server
A Python-based Model Context Protocol (MCP) server that enables LLMs to access external tools and resources through a standardized interface.
what is LangGraph MCP Server?
LangGraph MCP Server is a Python-based Model Context Protocol (MCP) server that allows large language models (LLMs) to access external tools and resources through a standardized interface.
how to use LangGraph MCP Server?
To use the LangGraph MCP Server, run the server using the command python -m app.server
and register your tools and resources as needed.
key features of LangGraph MCP Server?
- Modular architecture for easy maintenance and debugging.
- Centralized configuration management.
- Extensible tools and resources registration.
- Clean directory structure for better organization.
use cases of LangGraph MCP Server?
- Integrating various tools for LLMs to enhance their capabilities.
- Creating custom resources for specific applications.
- Facilitating communication between LLMs and external APIs.
FAQ from LangGraph MCP Server?
- What programming language is used for the MCP Server?
The MCP Server is implemented in Python.
- How can I add new tools or resources?
You can add new tools or resources by creating new files in the respective directories and registering them in the
__init__.py
files.
- Is the MCP Server scalable?
Yes, the architecture is designed to be scalable and maintainable as more functionality is added.
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
- Server: The main entry point that initializes the MCP server and registers all tools and resources.
- Config: Central location for all configuration settings.
- Tools: Functions that can be called by clients to perform specific tasks.
- Resources: Data sources that can be accessed by clients.
- Utils: Utility functions used throughout the application.
Adding New Functionality
Adding a New Tool
- Create a new file in the
app/tools/
directory (e.g.,weather_tools.py
). - Define your tool functions in this file.
- Create a registration function (e.g.,
register_weather_tools
). - 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
- Create a new file in the
app/resources/
directory (e.g.,weather_resources.py
). - Define your resource functions in this file.
- Create a registration function (e.g.,
register_weather_resources
). - 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
- Modularity: Each component has a single responsibility.
- Extensibility: Easy to add new tools and resources without modifying existing code.
- Maintainability: Organized structure makes debugging easier.
- Scalability: Can handle growth as more functionality is added.
- Testability: Components can be tested in isolation.
