Introduction to MCP Servers and Pydantic AI
Building a basic MCP server and interacting with Pydantic AI is a complex topic that can be broken down into simpler components. In this article, we’ll explore what MCP is, how to implement a simple server using Fast API, and how Pydantic AI fits into the picture.
What is MCP?
At a high level, an MCP server allows for a standardized way to define how a Large Language Model (LLM) interacts with tools. Instead of defining tools on a one-off basis in our LLM application, we can utilize prebuilt or custom servers that expose tools. This allows for both reusability for servers we may build ourselves or plugging into various vendor or open source MCP servers — preventing us from reinventing the wheel when we want to use a new tool.
Implementing a Simple MCP Server
For our MCP server, we’ll define one very basic tool — getting a user’s name. This allows us to hardcode a name and verify the LLM is picking up the information. To implement this server, we’ll use Fast API, a modern, fast (high-performance), web framework for building APIs with Python 3.7+ based on standard Python type hints.
Pydantic AI and MCP Clients
In our prior article on building a streaming approach with Pydantic AI, we built a pattern around streaming with API calls to Anthropic. In this article, we’ll look to expand to use Pydantic AI MCP Clients. Before implementing a connection to a MCP server via Pydantic AI, it’s essential to understand how MCP works and how Pydantic AI fits into the picture.
Further Reading and Resources
For more information on MCP, we recommend reading through Anthropic’s release post, the model context protocol site, and browsing through the python sdk github repo. These resources provide a deeper dive into the world of MCP and how it can be used to build more complex applications.
Conclusion
In conclusion, building a basic MCP server and interacting with Pydantic AI is a complex topic that requires a good understanding of MCP, Fast API, and Pydantic AI. By breaking down the components and understanding how they fit together, we can build more complex applications that utilize the power of LLMs and MCP servers.
FAQs
- What is MCP, and how does it work?
MCP is a standardized way to define how an LLM interacts with tools. It allows for reusability and prevents reinventing the wheel when using new tools. - What is Pydantic AI, and how does it fit into the picture?
Pydantic AI is a library that provides a simple way to interact with MCP servers. It allows us to build applications that utilize the power of LLMs and MCP servers. - What is Fast API, and how is it used in this article?
Fast API is a modern, fast (high-performance), web framework for building APIs with Python 3.7+ based on standard Python type hints. It is used to implement a simple MCP server in this article.
