Practical MCP: A Python Developer's Guide
Practical MCP: A Python Developer's Guide
From Theory to Production: Building and Deploying AI Tools for LLMs
About the Book
You've seen the incredible power of Large Language Models like Claude, GPT, and Gemini. But you've also hit the wall, watching them fail at simple tasks a junior developer could solve in minutes. These powerful AIs are trapped in a digital room with no tools and no access to the outside world. This book is about kicking down the door.
Practical MCP: A Python Developer's Guide is your hands-on guide to the Model Context Protocol (MCP), a groundbreaking specification for giving AI the tools it needs to interact with the real world. Think of it as the USB-C for AI—a universal standard that lets any model connect to any tool, from a simple Python function to a complex REST API or a private database.
This book takes you on a complete journey, from theory to production. You will learn to:
- Build Your First Tools: Master the two core Python libraries, `mcp` and `fastmcp`, to create local and remote MCP servers.
- Integrate with Major Platforms: Connect your custom tools to Anthropic's Claude, Google's Gemini, and OpenAI's ChatGPT.
- Master Advanced Interactions: Move beyond simple function calls. Create tools that can report progress, send logs, and even pause to ask the user for more information using elicitation.
- Secure and Deploy Your Servers: Learn to protect your tools with JWT authentication and deploy them to production environments like Google Cloud Run and traditional VMs using Docker and Nginx.
- Design Sophisticated Architectures: Seamlessly integrate MCP into existing web applications with ASGI, create powerful middleware, and automatically generate a full suite of tools from an OpenAPI specification.
- Build a Capstone Project: Put it all together by creating a full-stack AI Tarot reading application, orchestrating multiple MCP microservices to perform a complex, multi-step task.
- Connect to Real-World Services: Tackle the complexities of OAuth 2.0 to build tools that securely access a user's Google Docs and Google Drive, empowering your AI to act on their behalf.
By the end of this book, you won't just be using AI—you'll be extending it. You'll have the practical skills to build robust, secure, and scalable tools that unlock the true potential of Large Language Models.
Table of Contents
- Chapter 1: The Big Picture: Why MCP is a Game-Changer for AI
- The LLM’s Achilles’ Heel
- The Duct Tape Solution: Function Calling
- Enter MCP: A USB-C Port for AI
- A Tale of Two Libraries:
mcp
vs.fastmcp
- Key Takeaways
- Chapter 2: The “Hello, World” Experience: Your First MCP Servers
- Setting Up Your Pro Development Environment
- Creating a Basic Tool (
@mcp.tool
)
- Running Locally: The STDIO Transport
- Running Remotely: The HTTP Transport
- The MCP Inspector: Your Essential Debugging Partner
- Key Takeaways
- Chapter 3: Integrating with LLM Platforms
- Creating a Universal Tool
- From
localhost
to the World withngrok
- Integration with Anthropic (Claude)
- Integration with Google (Gemini)
- Integration with OpenAI (ChatGPT)
- Stateful vs. Stateless: A Tale of Two Servers
- Key Takeaways
- Chapter 4: Core MCP Components
- Resources and Prompts: The Foundation of Context
- The Context Object: Your Tool’s Superpowers
- Elicitation: Turning a Monologue into a Dialogue
- Key Takeaways
- Chapter 5: Authentication and Deployment of MCP Servers
- Authentication: Guarding Your Tools
- Deployment: Going Live
- Key Takeaways
- Chapter 6: Advanced Server Architecture
- Integrating with ASGI: Your MCP Server as a Citizen
- Supercharging Your Server with Middleware
- From OpenAPI to MCP in One Step
- Key Takeaways
- Chapter 7: Community Spotlights
- The Filesystem MCP Server
- The SQLite3 MCP Server
- The Fetching HTML MCP Server
- Key Takeaways
- Chapter 8: Capstone Project: The AI Tarot Oracle
- Project Overview: The AI Oracle
- Laying the Foundation: Data and Database
- Building the MCP Servers: The Tools of the Oracle
- The Brain of the Operation: The LLM Orchestrator
- The User Interface: A Gradio Front-End
- Putting It All Together: Running the Oracle
- Where to Go From Here?
- Chapter 9: Real-World Integration: The Google Workspace MCP Server
- Setup and Prerequisites
- Building the Server (
google_workspace_server.py
)
- Building the Client (
google_workspace_client.py
)
- The End-to-End Flow in Action
- Key Takeaways
- Chapter 1: The Big Picture: Why MCP is a Game-Changer for AI
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