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][pydantic_ai.mcp.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][pydantic_ai.toolsets.fastmcp.FastMCPToolset].
   3. Some model providers can themselves connect to remote MCP servers using a "built-in tool". Learn more about [MCPServerTool][pydantic_ai.builtin_tools.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