MCP Fundamentals 🧱/Elaboration

Hooray! You're all done! πŸ‘πŸ‘

What you've learned

1. MCP Basics & Ping

  • Core Concepts: You learned the foundational structure of the Model Context Protocol (MCP), which enables standardized communication between clients and servers.
  • Ping Tool: Implemented a simple ping tool to verify server connectivity using JSON-RPC, establishing the groundwork for MCP-compliant servers.

2. Tools

  • Tool Registration: Explored how to define and register server-side functions (tools) that clients can invoke.
  • Input Validation: Used JSON Schema (and Zod) to validate tool arguments.
  • Dynamic Responses: Built tools that return both static and dynamic results, and learned about trust, safety, and human-in-the-loop design.

3. Advanced Tools

  • Stateful Tools: Developed more complex tools that interact with a database, enabling persistent and retrievable data.
  • Code Organization: Refactored code for maintainability, using class-based server structures.
  • Error Handling: Implemented robust error handling to provide clear, MCP-compliant feedback to clients.

4. Resources

  • Resource Concept: Learned how to expose structured data (files, database records, etc.) as resources via MCP.
  • Resource URIs: Used unique URIs and metadata to identify and describe resources.
  • Resource Operations: Enabled clients to list, read, and (optionally) subscribe to resources, supporting both static and dynamic data.

5. Prompts

  • Reusable Prompts: Introduced MCP's prompt system for exposing reusable, parameterized instructions to clients and LLMs.
  • Prompt Registration: Registered prompts that guide model behavior, making workflows more accessible and consistent for users.

6. Sampling

  • Model Completions: Integrated MCP's sampling capability to request generative model completions (text, etc.) from clients.
  • Automation: Used sampling to automate tasks (e.g., suggesting tags), while keeping users in control of model selection and permissions.
  • Prompt Crafting: Practiced crafting effective prompts and structuring requests for reliability and safety.
In summary:
You've built a robust MCP server that supports tools, resources, prompts, and sampling. You've learned to design for extensibility, safety, and real-world use casesβ€”empowering both users and language models to interact with your server in powerful, structured ways.

Next Steps

Now you can build your own MCP server! Learn how to get the development environment up here:
Amazing work! πŸŽ‰
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