The video provides a step-by-step guide on building a Model Context Protocol (MCP) server using FastAPI to facilitate the integration of Large Language Model (LLM) agents with various tools, showcasing its setup, testing, and integration with the BeeAI framework. It emphasizes the server’s observability features for tracking tool usage and highlights its compatibility with both paid and free LLMs, demonstrating its versatility in AI applications.
The video provides a comprehensive guide on building a Model Context Protocol (MCP) server for integrating Large Language Model (LLM) agents with various tools and applications. Released by Anthropic in November 2024, the MCP aims to standardize the way LLMs communicate with tools, addressing the challenges of repetitive integrations across different frameworks. The presenter outlines the process of creating an MCP server in under ten minutes, discussing its compatibility with both paid and free LLMs, as well as its observability features for tracking tool usage.
In the first phase, the presenter demonstrates how to set up the MCP server using FastAPI. They begin by creating a project folder and a virtual environment, followed by installing necessary dependencies, including the MCP CLI package. The video showcases the creation of a basic server file, where the presenter imports required libraries and sets up the server using the FastMCP class. They also define a tool for predicting employee churn based on various attributes, such as years at the company and salary, which will be exposed to AI agents.
Once the server is established, the presenter moves to the second phase, which involves testing the server’s functionality. They start the development server to access the MCP inspector, allowing them to interact with the tools created. The presenter demonstrates how to connect to the inspector, list available tools, and test the predict churn tool by sending sample data. The successful predictions confirm that the server is functioning correctly, showcasing the ease of testing and interaction with the MCP server.
In the final phase, the presenter integrates the MCP server with an agent built using the BeeAI framework, specifically utilizing the Granite 3.1 LLM. They explain how to configure the agent to communicate with the MCP server and send employee data for churn prediction. The integration process is completed successfully, with the agent providing accurate predictions based on the input data. The presenter emphasizes the seamless interoperability of the MCP server with various agents and tools.
Additionally, the video touches on observability by highlighting the importance of logging tool calls within the server. The presenter explains how to implement logging to track tool usage effectively. They conclude by demonstrating the MCP server’s versatility, showing that it can be integrated with other platforms, such as Cursor, to further illustrate its broad applicability. Overall, the video serves as a practical guide for developers looking to streamline AI integration through the MCP framework.