Anthropic Model Context Protocol Certified Developer

I build AI integrations for a living, so the two Anthropic Model Context Protocol certifications were the natural step. The two Coursera courses, Introduction to Model Context Protocol and Advanced Topics, cover the full MCP lifecycle, from architecture to deployment, over about 7 hours of lessons and hands-on work.

What the courses cover

MCP replaces custom integrations, a different one for every service, with a single protocol an AI client can use to discover and use external capabilities. The first course starts there, and from the three things every integration rests on: tools (actions the model can perform), resources (structured data applications read) and prompts (reusable templates). The second adds the features that make a server production-ready: sampling, notifications and roots.

A server and a client in Python

The hands-on part is building an MCP server from scratch with the Python SDK, then a complete client that connects, discovers tools and resources and uses them. Testing with the official MCP Inspector was central, to see how messages flow and make debugging straightforward. Building both sides gives a clear picture of the whole lifecycle, from connection to tool invocation.

Transport protocols

The second course goes into the communication layer: STDIO for local development and StreamableHTTP for remote and distributed deployments, with the trade-offs between stateful connections, which keep every feature, and stateless configurations, which scale horizontally.

At HSPI and in my own projects

At HSPI I've built two MCP servers: I can't go into what they did, but it's the kind of integration between AI models and external services that MCP is built for. This ties into the work I do in my home infrastructure, where I experiment with LLMs and RAG systems.