mcp-course

This MCP server repository is an educational exploration of the Model Context Protocol (MCP). It outlines the core ideas behind MCP—providing structured context to language models through a server that exposes four core components: Tools, Resources, Prompts, and Samplings. While the README here focuses on learning and reference rather than a production-ready deployment, the general workflow remains: you run the MCP server, connect an MCP client, and use the server to expose interoperable tools and data sources that an AI model can query or interact with. The included concepts from the tutorial cover how servers are organized and how clients communicate with them to request contextual data, execute tools, and retrieve results to inform AI-driven decisions. Expect to encounter typical MCP concepts such as registering tools, defining resources, crafting prompts, and configuring samplings that guide how context is gathered and used by an AI model.

Prerequisites:

• Node.js (recommended for TypeScript/JavaScript MCP examples) or a runtime suitable for your chosen MCP server implementation
• Git to clone repositories
• Basic familiarity with the MCP concepts (Tools, Resources, Prompts, Samplings)

Installation steps:

1. Install Node.js and npm from https://nodejs.org/
2. Clone the repository: git clone https://github.com/your-organization/mcp-course.git
3. Navigate into the project directory: cd mcp-course
4. Install dependencies (if a package.json exists): npm install
5. Prepare and run the MCP server (adjust path to your server entry if needed): node path/to/server.js
6. If you’re using a containerized setup, build and run with your preferred container tooling (Docker, etc.)

Notes:

• If the project uses TypeScript, you may need to run a build step (e.g., npm run build) before starting the server.
• Ensure any required environment variables are set as described by your server’s configuration or README.

Tips and considerations:

• This repository appears to be an educational exploration of MCP rather than a production-ready server. Use it to learn the concepts and experiment with the basic server/client workflow.
• When working with MCP, clearly define Tools, Resources, Prompts, and Samplings to ensure your client can request the right contextual data and actions.
• Common issues often involve misconfigured endpoints, mismatched MCP versions between client and server, or missing environment variables. Keep dependencies aligned with the official MCP documentation you’re following.
• If you encounter integration questions, refer to the official MCP docs and the linked tutorial resource for step-by-step examples and patterns.