Model Context Protocol (MCP)

Pydantic AI supports Model Context Protocol (MCP) in multiple ways:

1. Agents can connect to MCP servers and use their tools using three different methods:
   1. Pydantic AI can act as an MCP client and connect directly to local and remote MCP servers. Learn more about MCPServer.
   2. Pydantic AI can use the FastMCP Client to connect to local and remote MCP servers, whether or not they're built using FastMCP Server. Learn more about FastMCPToolset.
   3. Some model providers can themselves connect to remote MCP servers using a "built-in tool". Learn more about MCPServerTool.
2. Agents can be used within MCP servers. Learn more

What is MCP?

The Model Context Protocol is a standardized protocol that allow AI applications (including programmatic agents like Pydantic AI, coding agents like cursor, and desktop applications like Claude Desktop) to connect to external tools and services using a common interface.

As with other protocols, the dream of MCP is that a wide range of applications can speak to each other without the need for specific integrations.

There is a great list of MCP servers at github.com/modelcontextprotocol/servers.

Some examples of what this means:

• Pydantic AI could use a web search service implemented as an MCP server to implement a deep research agent
• Cursor could connect to the Pydantic Logfire MCP server to search logs, traces and metrics to gain context while fixing a bug
• Pydantic AI, or any other MCP client could connect to our Run Python MCP server to run arbitrary Python code in a sandboxed environment