Brief Summary
This video introduces a REST MCP (Model Context Protocol) server developed to enable LLMs (Large Language Models) to interact with REST APIs for CRUD (Create, Retrieve, Update, Delete) operations. It covers the technology stack used (NodeJS, TypeScript, JavaScript), the framework's architecture, and a demonstration of integrating the MCP server with an AI tool (Claude) to perform REST API operations. The video explains MCP's role in providing context to LLMs, allowing them to interact with external data sources, and showcases how the developed server facilitates these interactions.
- The REST MCP server allows LLMs to perform CRUD operations on REST APIs.
- The server is built using NodeJS, TypeScript, and JavaScript, leveraging libraries like
mcp-serverandaxios. - A demonstration shows the integration of the server with the Claude AI tool, performing token generation, booking creation, updating, and deletion.
Introduction to REST MCP Server
The video introduces a new REST MCP server designed to allow LLMs to interact with REST APIs and perform CRUD operations. The server was developed from scratch using AIO's library. The presenter outlines the agenda to explain the server's functionality and its integration with LLMs.
Technology Stack and Framework Overview
The technology stack includes NodeJS, TypeScript, and JavaScript. Despite being more comfortable with Java, the presenter followed MCP documentation to develop the server. The mcp-server library is central, providing tools to define individual functionalities. J facilitates linking between methods and NLP, while Axios is used for performing CRUD operations. The framework's main class is index.ts, which uses the mcp-server library and stdio server transport for local execution. Server tools define individual functionalities, requiring NLP context for operations like token generation or user authentication.
Understanding Model Context Protocol (MCP)
MCP is a unified protocol that defines standards for applications to provide context to LLMs, enabling them to interact with external data sources. LLMs, the core of AI tools, require context to provide accurate outputs. MCP acts as a bridge between LLMs and external systems, following a client-server architecture. The client is any LLM capable of communicating with an MCP server. MCP stands for Model Context Protocol, where "Model" refers to the AI model (e.g., GPT-4). MCP defines a standard to provide context to the LLM, improving the accuracy of its output by connecting it to relevant data sources like Jira or REST APIs.
LLM Integration with REST MCP Server
The video discusses integrating an LLM with a REST MCP server to perform REST operations. Without the MCP server, an LLM cannot natively perform CRUD operations on REST APIs. The presenter shares resources, including a sample REST API with endpoints for token generation, booking management, and health checks. The GitHub repository and npm location for the MCP server code and node module are also provided.
Demonstration: Integrating with Claude and Performing CRUD Operations
The presenter demonstrates integrating the REST MCP server with the Claude AI tool. Initially, no MCP server is connected. The integration process involves installing Node.js and configuring Claude to run the MCP server locally. The presenter edits Claude's configuration file to include the MCP server details, which triggers the download and execution of the Node module. Once integrated, the HTTP server is visible in Claude's settings. The demonstration proceeds with performing CRUD operations using prompts. The AI tool successfully generates a token, creates a booking, updates the booking, and then deletes it, showcasing the MCP server's ability to facilitate these interactions.
