README Documentation
Part 1: Introduction & Context The MCP Hype vs. Reality The Model Context Protocol (MCP) has been generating lots of hype in the AI community lately, and for good reason. It represents a standardized way for LLMs to interact with external tools and services. But with all the excitement, it's important to cut through the hype and understand what MCP really is.
MCP isn't revolutionary new technology - it's a (potentially) revolutionary new standard. If you've been working with AI systems/agents for any length of time, you've already been implementing the core concept: giving LLMs access to tools through function calling. What's different is that MCP provides a standardized protocol for these interactions.
You may have seen demos showcasing how to integrate MCP servers with Claude Desktop or other AI assistants. While this is certainly a paradigm shift for personal AI, that's not our focus today. Instead, we're looking at how Python developers can build custom MCP servers and integrate them into production applications and agent systems.
Bridging the Gap There's a clear distinction between:
Personal MCP use - Integrating servers with Claude Desktop, Cursor, or other personal AI assistants Backend integration - Building MCP into your Python applications and agent systems Most tutorials online cover the first case, showing you how to enhance your personal AI tools. This is valuable, but leaves a gap for developers building production systems who need to understand the deeper technical aspects of working with MCP in their Python applications.
In this crash course, we're focusing specifically on Python developers who want to:
Understand the technical architecture of MCP Build custom MCP servers using the Python SDK Integrate these servers into Python applications Make informed decisions about when and how to use MCP