Summary
In this episode of the AI Engineering Podcast Jeremiah Lowin, founder and CEO of Prefect Technologies, talks about the FastMCP framework and the design of MCP servers. Jeremiah explains the evolution of FastMCP, from its initial creation as a simpler alternative to the MCP SDK to its current role in facilitating the deployment of AI tools. The discussion covers the complexities of designing MCP servers, the importance of context engineering, and the potential pitfalls of overwhelming AI agents with too many tools. Jeremiah also highlights the importance of simplicity and incremental adoption in software design, and shares insights into the future of MCP and the broader AI ecosystem. The episode concludes with a look at the challenges of authentication and authorization in AI applications and the exciting potential of MCP as a protocol for the future of AI-driven business logic.

Announcements

Hello and welcome to the AI Engineering Podcast, your guide to the fast-moving world of building scalable and maintainable AI systems
Your host is Tobias Macey and today I'm interviewing Jeremiah Lowin about the FastMCP framework and how to design and build your own MCP servers

Interview

Introduction
How did you get involved in machine learning?
Can you start by describing what MCP is and its purpose in the ecosystem of AI applications?
What is FastMCP and what motivated you to create it?
- Recognizing that MCP is relatively young, how would you characterize the landscape of MCP frameworks?
What are some of the stumbling blocks on the path to building a well engineered MCP server?
- What are the potential ramifications of poorly designed and implemented MCP implementations?
In the overall context of an AI-powered/agentic application, what are the tradeoffs of investing in the MCP protocol? (e.g. engineering effort, process isolation, tool creation, auth(n|z), etc.)
- In your experience, what are the architectural patterns that you see of MCP implementation and usage?
There are a multitude of MCP servers available for a variety of use cases. What are the key factors that someone should be using to evaluate their viability for a production use case?
Can you give an overview of the key characteristics of FastMCP and why someone might select it as their implementation target for a custom MCP server?
- How have the design, scope, and goals of the project evolved since you first started working on it?
For someone who is using FastMCP as the framework for creating their own AI tools, what are some of the design considerations or best practices that they should be aware of?
- What are some of the ways that someone might consider integrating FastMCP into their existing Python-powered web applications (e.g. FastAPI, Django, Flask, etc.)
As you continue to invest your time and energy into FastMCP, what is your overall goal for the project?
What are the most interesting, innovative, or unexpected ways that you have seen FastMCP used?
What are the most interesting, unexpected, or challenging lessons that you have learned while working on FastMCP?
When is FastMCP the wrong choice?
What do you have planned for the future of FastMCP?

Contact Info

Parting Question

From your perspective, what are the biggest gaps in tooling, technology, or training for AI systems today?

Closing Announcements

Thank you for listening! Don't forget to check out our other shows. The Data Engineering Podcast covers the latest on modern data management. Podcast.__init__ covers the Python language, its community, and the innovative ways it is being used.
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Links

The intro and outro music is from Hitman's Lovesong feat. Paola Graziano by The Freak Fandango Orchestra/CC BY-SA 3.0

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