Author(s): Barrett Studdard

Originally published on Towards AI.

Building a basic MCP server and interacting with PydanticAICredit to Kenny Eliason on Unsplash

In my prior article on building a streaming approach with Pydantic AI, I 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, let’s review what MCP is at a very high level as well as implement a simple server with Fast API.

At a high level, an MCP server allows for a standardized way to define how an 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.

For more information, I’d recommend reading through Anthropic’s release post, the model context protocol site, and browsing through the python sdk github repo.

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… Read the full blog for free on Medium.

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Published via Towards AI