ThirdBrAIn.tech

How to Build an MCP Server for LLM Agents Simplify AI Integration

2 min read

How to Build an MCP Server for LLM Agents Simplify AI Integration

AI Summary

Summary of MCP Server Construction Video

  1. Introduction
    • Purpose: Build an MCP server to connect LLM agents.
    • Context: Model Context Protocol (MCP) released by Anthropic in Nov 2024.
  2. Challenges Addressed by MCP
    • Standardization of how LLMs communicate with tools.
    • Elimination of repetitive integration for AI capabilities.
  3. Building the MCP Server
    • Setup: Guidelines given to build in under 10 minutes.
    • Phase 1: Server Creation
      • Start by creating a project directory and virtual environment.
      • Install dependencies: MCP CLI and requests.
      • Create server.py and import necessary libraries using FastAPI and MCP packages.
      • Define a tool for predicting employee churn based on attributes like tenure and salary.
  4. Testing the MCP Server
    • Start the dev server to access the MCP inspector.
    • Demonstrate using the tools in the inspector to test predictions based on employee data.
    • Different transport types discussed: standard input/output (STDIO) vs. server-sent events (SSE).
  5. Integration with Agent
    • Integration of MCP server into an agent using the BeeAI framework.
    • Use the Granite 3.1 model for predictions of employee churn.
    • Execute predictions with appropriate user queries.
  6. Observability
    • Implementation of logging to track tool calls.
    • MCP server can be utilized across various platforms like Cursor.
  7. Conclusion
    • Successfully built and integrated the MCP server with agent capabilities, demonstrating its functionality.

Graph View