User Management MCP Server

# Model Context Protocol (MCP) Learning Notes ## Video Reference - **Source**: [Web Dev Simplified YouTube Video](https://youtu.be/ZoZxQwp1PiM?si=F2Wb-wrBPOkBmKkz) - **Credits**: Web Dev Simplified (YouTube channel) ## What is MCP? Model Context Protocol (MCP) is a protocol that defines how a client (such as an LLM) can communicate and use tools and resources defined at the server level. It implements a client-server architecture with the following components: - Tools - Resources - Prompts - Samplings ## Documentation & Resources - Official documentation: [modelcontextprotocol.io/introduction](https://modelcontextprotocol.io/introduction) - Includes starter projects in multiple languages for MCP client and server implementation ## Implementation Details ### Server Setup The `src/server.ts` file contains the code for creating an MCP server and defining tools, resources, and prompts. ### Testing the Implementation 1. Build the server: ```bash npm run server:build ``` 2. Add to VS Code using the "Add MCP server" command 3. Access server functionality in the Copilot chat UI 4. Use "#" followed by tool name to access implemented tools  ### Client Implementation Note: to use the query and prompts from the client you will need a gemini ai api key, you can add this in the .env file The `src/client.ts` file provides a CLI client for interacting with the MCP server. It connects to the server, lists available tools, resources, and prompts, and allows you to: - Query the LLM directly - Run tools (with parameter input) - Access resources (with dynamic URI parameters) - Use prompts (with argument input) #### How it works 1. Connects to the MCP server using a transport layer. 2. Fetches available tools, resources, prompts, and resource templates. 3. Presents a menu for the user to select an action: Query, Tools, Resources, or Prompts. 4. Handles each action: - **Query**: Sends a prompt to the LLM and optionally invokes tools. - **Tools**: Lets you select and run a tool, entering parameters as needed. - **Resources**: Lets you select a resource or template, entering URI parameters if required, and displays the result. - **Prompts**: Lets you select a prompt, enter arguments, and view the generated output. 5. For prompts, you can choose to run the generated text through the LLM for further results. #### Example Usage When you run the client, you'll see a menu: ``` What would you like to do ❯ Query Tools Resources Prompts ``` Selecting an option will guide you through the available features interactively. #### Resources - **users**: Retrieves all users from the JSON file - **user-details**: Retrieves user details by ID #### Tools - **create-user**: Creates a new user with the following parameters: - username - email - address - age - phone number - **create-random-user**: Generates and creates a random user #### Prompts - **generate-fake-user**: Prompt with fixed fields for generating fake user data #### Sampling - **create-random-user**: This uses sampling i.e. calling requests on the LLM or clientto generate something.