AI Engineer’s Handbook to MCP Architecture

Author(s): Vatsal Saglani

Originally published on Towards AI.

Part 1: Introduction to MCPBlog Header Generated by the Author

In the last blog, I vented about the noise; in this blog we start writing code. In plain terms, MCP is the wiring (connection layer) that lets a language-model call our software without stuffing every command into the prompt. We have already seen a lot of posts about MCP servers: they are just small web services that tell the model which tools exist, then run those tools when asked.

In this first build log, we zoom out, look at MCP from 10,000 feet, name each moving part, and then roll up our sleeves to write the tiniest possible MCP server with Python + FastAPI. We’ll compare it with the vanilla Function Calling flow and see where the two overlap and where they part ways.

I’m sure that a lot of us have tried adding tools to GPT or Claude the “old” way, we know the dance:

add the function schemas to the tools parameter,all the LLMs don’t support tools by default so write a tool identification and argument wrapper on top of the LLM completion call,hope the model/wrapper returns valid JSON,verify the JSON, if incorrect retry, or patch a regex when it doesn’t

This works until we need five… Read the full blog for free on Medium.

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Published via Towards AI