Skip to content

pydantic_ai.mcp

MCPServer

Bases: ABC

Base class for attaching agents to MCP servers.

See https://modelcontextprotocol.io for more information.

Source code in pydantic_ai_slim/pydantic_ai/mcp.py 
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
class MCPServer(ABC):
    """Base class for attaching agents to MCP servers.

    See <https://modelcontextprotocol.io> for more information.
    """

    is_running: bool = False

    _client: ClientSession
    _read_stream: MemoryObjectReceiveStream[JSONRPCMessage | Exception]
    _write_stream: MemoryObjectSendStream[JSONRPCMessage]
    _exit_stack: AsyncExitStack

    @abstractmethod
    @asynccontextmanager
    async def client_streams(
        self,
    ) -> AsyncIterator[
        tuple[MemoryObjectReceiveStream[JSONRPCMessage | Exception], MemoryObjectSendStream[JSONRPCMessage]]
    ]:
        """Create the streams for the MCP server."""
        raise NotImplementedError('MCP Server subclasses must implement this method.')
        yield

    async def list_tools(self) -> list[ToolDefinition]:
        """Retrieve tools that are currently active on the server.

        Note:
        - We don't cache tools as they might change.
        - We also don't subscribe to the server to avoid complexity.
        """
        tools = await self._client.list_tools()
        return [
            ToolDefinition(
                name=tool.name,
                description=tool.description or '',
                parameters_json_schema=tool.inputSchema,
            )
            for tool in tools.tools
        ]

    async def call_tool(self, tool_name: str, arguments: dict[str, Any]) -> CallToolResult:
        """Call a tool on the server.

        Args:
            tool_name: The name of the tool to call.
            arguments: The arguments to pass to the tool.

        Returns:
            The result of the tool call.
        """
        return await self._client.call_tool(tool_name, arguments)

    async def __aenter__(self) -> Self:
        self._exit_stack = AsyncExitStack()

        self._read_stream, self._write_stream = await self._exit_stack.enter_async_context(self.client_streams())
        client = ClientSession(read_stream=self._read_stream, write_stream=self._write_stream)
        self._client = await self._exit_stack.enter_async_context(client)

        await self._client.initialize()
        self.is_running = True
        return self

    async def __aexit__(
        self, exc_type: type[BaseException] | None, exc_value: BaseException | None, traceback: TracebackType | None
    ) -> bool | None:
        await self._exit_stack.aclose()
        self.is_running = False

client_streams abstractmethod async 

client_streams() -> AsyncIterator[
    tuple[
        MemoryObjectReceiveStream[
            JSONRPCMessage | Exception
        ],
        MemoryObjectSendStream[JSONRPCMessage],
    ]
]

Create the streams for the MCP server.

Source code in pydantic_ai_slim/pydantic_ai/mcp.py 
43
44
45
46
47
48
49
50
51
52
@abstractmethod
@asynccontextmanager
async def client_streams(
    self,
) -> AsyncIterator[
    tuple[MemoryObjectReceiveStream[JSONRPCMessage | Exception], MemoryObjectSendStream[JSONRPCMessage]]
]:
    """Create the streams for the MCP server."""
    raise NotImplementedError('MCP Server subclasses must implement this method.')
    yield

list_tools async 

list_tools() -> list[ToolDefinition]

Retrieve tools that are currently active on the server.

Note: - We don't cache tools as they might change. - We also don't subscribe to the server to avoid complexity.

Source code in pydantic_ai_slim/pydantic_ai/mcp.py 
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
async def list_tools(self) -> list[ToolDefinition]:
    """Retrieve tools that are currently active on the server.

    Note:
    - We don't cache tools as they might change.
    - We also don't subscribe to the server to avoid complexity.
    """
    tools = await self._client.list_tools()
    return [
        ToolDefinition(
            name=tool.name,
            description=tool.description or '',
            parameters_json_schema=tool.inputSchema,
        )
        for tool in tools.tools
    ]

call_tool async 

call_tool(
    tool_name: str, arguments: dict[str, Any]
) -> CallToolResult

Call a tool on the server.

Parameters:

Name Type Description Default
tool_name str

The name of the tool to call.

 required
arguments dict[str, Any]

The arguments to pass to the tool.

 required

Returns:

Type Description
CallToolResult

The result of the tool call.
Source code in pydantic_ai_slim/pydantic_ai/mcp.py 
71
72
73
74
75
76
77
78
79
80
81
async def call_tool(self, tool_name: str, arguments: dict[str, Any]) -> CallToolResult:
    """Call a tool on the server.

    Args:
        tool_name: The name of the tool to call.
        arguments: The arguments to pass to the tool.

    Returns:
        The result of the tool call.
    """
    return await self._client.call_tool(tool_name, arguments)

MCPServerStdio dataclass

Bases: MCPServer

Runs an MCP server in a subprocess and communicates with it over stdin/stdout.

This class implements the stdio transport from the MCP specification. See https://spec.modelcontextprotocol.io/specification/2024-11-05/basic/transports/#stdio for more information.

Note

Using this class as an async context manager will start the server as a subprocess when entering the context, and stop it when exiting the context.

Example: 

from pydantic_ai import Agent
from pydantic_ai.mcp import MCPServerStdio

server = MCPServerStdio('npx', ['-y', '@pydantic/mcp-run-python', 'stdio'])  # (1)!
agent = Agent('openai:gpt-4o', mcp_servers=[server])

async def main():
    async with agent.run_mcp_servers():  # (2)!
        ...

  1. See MCP Run Python for more information.
  2. This will start the server as a subprocess and connect to it.
Source code in pydantic_ai_slim/pydantic_ai/mcp.py 
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
@dataclass
class MCPServerStdio(MCPServer):
    """Runs an MCP server in a subprocess and communicates with it over stdin/stdout.

    This class implements the stdio transport from the MCP specification.
    See <https://spec.modelcontextprotocol.io/specification/2024-11-05/basic/transports/#stdio> for more information.

    !!! note
        Using this class as an async context manager will start the server as a subprocess when entering the context,
        and stop it when exiting the context.

    Example:
    ```python {py="3.10"}
    from pydantic_ai import Agent
    from pydantic_ai.mcp import MCPServerStdio

    server = MCPServerStdio('npx', ['-y', '@pydantic/mcp-run-python', 'stdio'])  # (1)!
    agent = Agent('openai:gpt-4o', mcp_servers=[server])

    async def main():
        async with agent.run_mcp_servers():  # (2)!
            ...
    ```

    1. See [MCP Run Python](../mcp/run-python.md) for more information.
    2. This will start the server as a subprocess and connect to it.
    """

    command: str
    """The command to run."""

    args: Sequence[str]
    """The arguments to pass to the command."""

    env: dict[str, str] | None = None
    """The environment variables the CLI server will have access to.

    By default the subprocess will not inherit any environment variables from the parent process.
    If you want to inherit the environment variables from the parent process, use `env=os.environ`.
    """

    @asynccontextmanager
    async def client_streams(
        self,
    ) -> AsyncIterator[
        tuple[MemoryObjectReceiveStream[JSONRPCMessage | Exception], MemoryObjectSendStream[JSONRPCMessage]]
    ]:
        server = StdioServerParameters(command=self.command, args=list(self.args), env=self.env)
        async with stdio_client(server=server) as (read_stream, write_stream):
            yield read_stream, write_stream

command instance-attribute 

command: str

The command to run.

args instance-attribute 

args: Sequence[str]

The arguments to pass to the command.

env class-attribute instance-attribute 

env: dict[str, str] | None = None

The environment variables the CLI server will have access to.

By default the subprocess will not inherit any environment variables from the parent process. If you want to inherit the environment variables from the parent process, use env=os.environ.

MCPServerHTTP dataclass

Bases: MCPServer

An MCP server that connects over streamable HTTP connections.

This class implements the SSE transport from the MCP specification. See https://spec.modelcontextprotocol.io/specification/2024-11-05/basic/transports/#http-with-sse for more information.

The name "HTTP" is used since this implemented will be adapted in future to use the new Streamable HTTP currently in development.

Note

Using this class as an async context manager will create a new pool of HTTP connections to connect to a server which should already be running.

Example: 

from pydantic_ai import Agent
from pydantic_ai.mcp import MCPServerHTTP

server = MCPServerHTTP('http://localhost:3001/sse')  # (1)!
agent = Agent('openai:gpt-4o', mcp_servers=[server])

async def main():
    async with agent.run_mcp_servers():  # (2)!
        ...

  1. E.g. you might be connecting to a server run with npx @pydantic/mcp-run-python sse, see MCP Run Python for more information.
  2. This will connect to a server running on localhost:3001.
Source code in pydantic_ai_slim/pydantic_ai/mcp.py 
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
@dataclass
class MCPServerHTTP(MCPServer):
    """An MCP server that connects over streamable HTTP connections.

    This class implements the SSE transport from the MCP specification.
    See <https://spec.modelcontextprotocol.io/specification/2024-11-05/basic/transports/#http-with-sse> for more information.

    The name "HTTP" is used since this implemented will be adapted in future to use the new
    [Streamable HTTP](https://github.com/modelcontextprotocol/specification/pull/206) currently in development.

    !!! note
        Using this class as an async context manager will create a new pool of HTTP connections to connect
        to a server which should already be running.

    Example:
    ```python {py="3.10"}
    from pydantic_ai import Agent
    from pydantic_ai.mcp import MCPServerHTTP

    server = MCPServerHTTP('http://localhost:3001/sse')  # (1)!
    agent = Agent('openai:gpt-4o', mcp_servers=[server])

    async def main():
        async with agent.run_mcp_servers():  # (2)!
            ...
    ```

    1. E.g. you might be connecting to a server run with `npx @pydantic/mcp-run-python sse`,
      see [MCP Run Python](../mcp/run-python.md) for more information.
    2. This will connect to a server running on `localhost:3001`.
    """

    url: str
    """The URL of the SSE endpoint on the MCP server.

    For example for a server running locally, this might be `http://localhost:3001/sse`.
    """

    @asynccontextmanager
    async def client_streams(
        self,
    ) -> AsyncIterator[
        tuple[MemoryObjectReceiveStream[JSONRPCMessage | Exception], MemoryObjectSendStream[JSONRPCMessage]]
    ]:  # pragma: no cover
        async with sse_client(url=self.url) as (read_stream, write_stream):
            yield read_stream, write_stream

url instance-attribute 

url: str

The URL of the SSE endpoint on the MCP server.

For example for a server running locally, this might be http://localhost:3001/sse.