MCP Crash Course
by Ayyappa054
This project demonstrates the integration of LangChain with Model Control Protocol (MCP) adapters, showcasing a system that handles mathematical calculations and weather queries through separate MCP servers. It provides examples of both single and multi-server implementations.
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What is MCP Crash Course?
This project is a demonstration of how to integrate LangChain with Model Control Protocol (MCP) adapters to create a system that can handle different types of queries using multiple MCP servers. It includes examples for both single-server and multi-server setups.
How to use MCP Crash Course?
To use this project, clone the repository, create a virtual environment, install the dependencies, and set up your OpenAI API key in a .env file. Then, you can run either the single-server example (python main.py) or the multi-server example (python langchain_client.py).
Key features of MCP Crash Course
Multiple MCP server integration (math and weather servers)
LangChain integration with OpenAI
Async operation support
Environment variable configuration
Use cases of MCP Crash Course
Building AI agents that can interact with multiple specialized services
Creating systems that can handle different types of queries using different models
Demonstrating the use of MCP adapters with LangChain
Experimenting with different agent architectures and tool selection strategies
FAQ from MCP Crash Course
What is MCP?
What is MCP?
Model Control Protocol (MCP) is a protocol for managing and controlling machine learning models.
What is LangChain?
What is LangChain?
LangChain is a framework for developing applications powered by language models.
What is the purpose of this project?
What is the purpose of this project?
This project demonstrates how to integrate LangChain with MCP adapters to create a system that can handle different types of queries using multiple MCP servers.
What are the prerequisites for running this project?
What are the prerequisites for running this project?
You need Python 3.x and an OpenAI API key.
How do I run the multi-server example?
How do I run the multi-server example?
Run the command python langchain_client.py after setting up the environment and dependencies.