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AI Engineer’s Handbook to MCP Architecture
Artificial Intelligence   Data Science   Latest   Machine Learning

AI Engineer’s Handbook to MCP Architecture

Last Updated on April 28, 2025 by Editorial Team

Author(s): Vatsal Saglani

Originally published on Towards AI.

Part 3: The Minimal MCP ClientAI Engineer’s Handbook to MCP ArchitectureBlog Header Generated by the Author

In the previous blog, we built two specialized servers: a File Management Server for document operations and a Run Command Server for shell execution. In this blog, we’ll develop the crucial bridge component, the MCP Client hub that connects language models to these servers.

Image Generated by Author

We’ll be building this hub using FastAPI. Below, we provide a quick survey of existing MCP clients, starting with Claude Desktop’s native tool support. Then we’ll implement a minimal hub with a clean separation between discovery and execution. Our MCP Client hub will be built from first principles with an emphasis on simplicity and transparency, using FastAPI, Pydantic, and httpx.

Explore the entire codebase of the MCP Client Hub in the GitHub repo below:

Contribute to vatsalsaglani/nano-MCP development by creating an account on GitHub.

github.com

Anthropic’s Claude Desktop includes native MCP client capabilities. If we search the public MCP repos and Anthropic’s docs we will not find any reference code related to the MCP client being used in Claude Desktop.

Users enable “MCP Clients” in Labs settings, configuring servers via claude_desktop_config.json. Claude Desktop then handles tool discovery and routing over HTTP.

Several open-source alternatives have emerged:

mcp-use: Python package for MCP server interaction (pip install mcp-use)Dolphin-MCP:… Read the full blog for free on Medium.

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