from __future__ import annotations import base64 import functools import os import re import warnings from abc import ABC, abstractmethod from asyncio import Lock from collections.abc import AsyncIterator, Awaitable, Callable, Sequence from contextlib import AbstractAsyncContextManager, AsyncExitStack, asynccontextmanager from dataclasses import field, replace from datetime import timedelta from pathlib import Path from typing import Annotated, Any import anyio import httpx import pydantic_core from anyio.streams.memory import MemoryObjectReceiveStream, MemoryObjectSendStream from pydantic import BaseModel, Discriminator, Field, Tag from pydantic_core import CoreSchema, core_schema from typing_extensions import Self, assert_never, deprecated from pydantic_ai.tools import RunContext, ToolDefinition from .direct import model_request from .toolsets.abstract import AbstractToolset, ToolsetTool try: from mcp import types as mcp_types from mcp.client.session import ClientSession, ElicitationFnT, LoggingFnT from mcp.client.sse import sse_client from mcp.client.stdio import StdioServerParameters, stdio_client from mcp.client.streamable_http import GetSessionIdCallback, streamablehttp_client from mcp.shared.context import RequestContext from mcp.shared.exceptions import McpError from mcp.shared.message import SessionMessage except ImportError as _import_error: raise ImportError( 'Please install the `mcp` package to use the MCP server, ' 'you can use the `mcp` optional group — `pip install "pydantic-ai-slim[mcp]"`' ) from _import_error # after mcp imports so any import error maps to this file, not _mcp.py from . import _mcp, _utils, exceptions, messages, models __all__ = 'MCPServer', 'MCPServerStdio', 'MCPServerHTTP', 'MCPServerSSE', 'MCPServerStreamableHTTP', 'load_mcp_servers' TOOL_SCHEMA_VALIDATOR = pydantic_core.SchemaValidator( schema=pydantic_core.core_schema.dict_schema( pydantic_core.core_schema.str_schema(), pydantic_core.core_schema.any_schema() ) ) # Environment variable expansion pattern # Supports both ${VAR_NAME} and ${VAR_NAME:-default} syntax # Group 1: variable name # Group 2: the ':-' separator (to detect if default syntax is used) # Group 3: the default value (can be empty) _ENV_VAR_PATTERN = re.compile(r'\$\{([^}:]+)(:-([^}]*))?\}') class MCPServer(AbstractToolset[Any], ABC): """Base class for attaching agents to MCP servers. See for more information. """ tool_prefix: str | None """A prefix to add to all tools that are registered with the server. If not empty, will include a trailing underscore(`_`). e.g. if `tool_prefix='foo'`, then a tool named `bar` will be registered as `foo_bar` """ log_level: mcp_types.LoggingLevel | None """The log level to set when connecting to the server, if any. See for more details. If `None`, no log level will be set. """ log_handler: LoggingFnT | None """A handler for logging messages from the server.""" timeout: float """The timeout in seconds to wait for the client to initialize.""" read_timeout: float """Maximum time in seconds to wait for new messages before timing out. This timeout applies to the long-lived connection after it's established. If no new messages are received within this time, the connection will be considered stale and may be closed. Defaults to 5 minutes (300 seconds). """ process_tool_call: ProcessToolCallback | None """Hook to customize tool calling and optionally pass extra metadata.""" allow_sampling: bool """Whether to allow MCP sampling through this client.""" sampling_model: models.Model | None """The model to use for sampling.""" max_retries: int """The maximum number of times to retry a tool call.""" elicitation_callback: ElicitationFnT | None = None """Callback function to handle elicitation requests from the server.""" _id: str | None _enter_lock: Lock = field(compare=False) _running_count: int _exit_stack: AsyncExitStack | None _client: ClientSession _read_stream: MemoryObjectReceiveStream[SessionMessage | Exception] _write_stream: MemoryObjectSendStream[SessionMessage] _server_info: mcp_types.Implementation def __init__( self, tool_prefix: str | None = None, log_level: mcp_types.LoggingLevel | None = None, log_handler: LoggingFnT | None = None, timeout: float = 5, read_timeout: float = 5 * 60, process_tool_call: ProcessToolCallback | None = None, allow_sampling: bool = True, sampling_model: models.Model | None = None, max_retries: int = 1, elicitation_callback: ElicitationFnT | None = None, *, id: str | None = None, ): self.tool_prefix = tool_prefix self.log_level = log_level self.log_handler = log_handler self.timeout = timeout self.read_timeout = read_timeout self.process_tool_call = process_tool_call self.allow_sampling = allow_sampling self.sampling_model = sampling_model self.max_retries = max_retries self.elicitation_callback = elicitation_callback self._id = id or tool_prefix self.__post_init__() def __post_init__(self): self._enter_lock = Lock() self._running_count = 0 self._exit_stack = None @abstractmethod @asynccontextmanager async def client_streams( self, ) -> AsyncIterator[ tuple[ MemoryObjectReceiveStream[SessionMessage | Exception], MemoryObjectSendStream[SessionMessage], ] ]: """Create the streams for the MCP server.""" raise NotImplementedError('MCP Server subclasses must implement this method.') yield @property def id(self) -> str | None: return self._id @id.setter def id(self, value: str | None): self._id = value @property def label(self) -> str: if self.id: return super().label # pragma: no cover else: return repr(self) @property def tool_name_conflict_hint(self) -> str: return 'Set the `tool_prefix` attribute to avoid name conflicts.' @property def server_info(self) -> mcp_types.Implementation: """Access the information send by the MCP server during initialization.""" if getattr(self, '_server_info', None) is None: raise AttributeError( f'The `{self.__class__.__name__}.server_info` is only instantiated after initialization.' ) return self._server_info async def list_tools(self) -> list[mcp_types.Tool]: """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. """ async with self: # Ensure server is running result = await self._client.list_tools() return result.tools async def direct_call_tool( self, name: str, args: dict[str, Any], metadata: dict[str, Any] | None = None, ) -> ToolResult: """Call a tool on the server. Args: name: The name of the tool to call. args: The arguments to pass to the tool. metadata: Request-level metadata (optional) Returns: The result of the tool call. Raises: ModelRetry: If the tool call fails. """ async with self: # Ensure server is running try: result = await self._client.send_request( mcp_types.ClientRequest( mcp_types.CallToolRequest( method='tools/call', params=mcp_types.CallToolRequestParams( name=name, arguments=args, _meta=mcp_types.RequestParams.Meta(**metadata) if metadata else None, ), ) ), mcp_types.CallToolResult, ) except McpError as e: raise exceptions.ModelRetry(e.error.message) if result.isError: message: str | None = None if result.content: # pragma: no branch text_parts = [part.text for part in result.content if isinstance(part, mcp_types.TextContent)] message = '\n'.join(text_parts) raise exceptions.ModelRetry(message or 'MCP tool call failed') # Prefer structured content if there are only text parts, which per the docs would contain the JSON-encoded structured content for backward compatibility. # See https://github.com/modelcontextprotocol/python-sdk#structured-output if (structured := result.structuredContent) and not any( not isinstance(part, mcp_types.TextContent) for part in result.content ): # The MCP SDK wraps primitives and generic types like list in a `result` key, but we want to use the raw value returned by the tool function. # See https://github.com/modelcontextprotocol/python-sdk#structured-output if isinstance(structured, dict) and len(structured) == 1 and 'result' in structured: return structured['result'] return structured mapped = [await self._map_tool_result_part(part) for part in result.content] return mapped[0] if len(mapped) == 1 else mapped async def call_tool( self, name: str, tool_args: dict[str, Any], ctx: RunContext[Any], tool: ToolsetTool[Any], ) -> ToolResult: if self.tool_prefix: name = name.removeprefix(f'{self.tool_prefix}_') ctx = replace(ctx, tool_name=name) if self.process_tool_call is not None: return await self.process_tool_call(ctx, self.direct_call_tool, name, tool_args) else: return await self.direct_call_tool(name, tool_args) async def get_tools(self, ctx: RunContext[Any]) -> dict[str, ToolsetTool[Any]]: return { name: self.tool_for_tool_def( ToolDefinition( name=name, description=mcp_tool.description, parameters_json_schema=mcp_tool.inputSchema, metadata={ 'meta': mcp_tool.meta, 'annotations': mcp_tool.annotations.model_dump() if mcp_tool.annotations else None, 'output_schema': mcp_tool.outputSchema or None, }, ), ) for mcp_tool in await self.list_tools() if (name := f'{self.tool_prefix}_{mcp_tool.name}' if self.tool_prefix else mcp_tool.name) } def tool_for_tool_def(self, tool_def: ToolDefinition) -> ToolsetTool[Any]: return ToolsetTool( toolset=self, tool_def=tool_def, max_retries=self.max_retries, args_validator=TOOL_SCHEMA_VALIDATOR, ) async def __aenter__(self) -> Self: """Enter the MCP server context. This will initialize the connection to the server. If this server is an [`MCPServerStdio`][pydantic_ai.mcp.MCPServerStdio], the server will first be started as a subprocess. This is a no-op if the MCP server has already been entered. """ async with self._enter_lock: if self._running_count == 0: async with AsyncExitStack() as exit_stack: self._read_stream, self._write_stream = await exit_stack.enter_async_context(self.client_streams()) client = ClientSession( read_stream=self._read_stream, write_stream=self._write_stream, sampling_callback=self._sampling_callback if self.allow_sampling else None, elicitation_callback=self.elicitation_callback, logging_callback=self.log_handler, read_timeout_seconds=timedelta(seconds=self.read_timeout), ) self._client = await exit_stack.enter_async_context(client) with anyio.fail_after(self.timeout): result = await self._client.initialize() self._server_info = result.serverInfo if log_level := self.log_level: await self._client.set_logging_level(log_level) self._exit_stack = exit_stack.pop_all() self._running_count += 1 return self async def __aexit__(self, *args: Any) -> bool | None: if self._running_count == 0: raise ValueError('MCPServer.__aexit__ called more times than __aenter__') async with self._enter_lock: self._running_count -= 1 if self._running_count == 0 and self._exit_stack is not None: await self._exit_stack.aclose() self._exit_stack = None @property def is_running(self) -> bool: """Check if the MCP server is running.""" return bool(self._running_count) async def _sampling_callback( self, context: RequestContext[ClientSession, Any], params: mcp_types.CreateMessageRequestParams ) -> mcp_types.CreateMessageResult | mcp_types.ErrorData: """MCP sampling callback.""" if self.sampling_model is None: raise ValueError('Sampling model is not set') # pragma: no cover pai_messages = _mcp.map_from_mcp_params(params) model_settings = models.ModelSettings() if max_tokens := params.maxTokens: # pragma: no branch model_settings['max_tokens'] = max_tokens if temperature := params.temperature: # pragma: no branch model_settings['temperature'] = temperature if stop_sequences := params.stopSequences: # pragma: no branch model_settings['stop_sequences'] = stop_sequences model_response = await model_request(self.sampling_model, pai_messages, model_settings=model_settings) return mcp_types.CreateMessageResult( role='assistant', content=_mcp.map_from_model_response(model_response), model=self.sampling_model.model_name, ) async def _map_tool_result_part( self, part: mcp_types.ContentBlock ) -> str | messages.BinaryContent | dict[str, Any] | list[Any]: # See https://github.com/jlowin/fastmcp/blob/main/docs/servers/tools.mdx#return-values if isinstance(part, mcp_types.TextContent): text = part.text if text.startswith(('[', '{')): try: return pydantic_core.from_json(text) except ValueError: pass return text elif isinstance(part, mcp_types.ImageContent): return messages.BinaryContent(data=base64.b64decode(part.data), media_type=part.mimeType) elif isinstance(part, mcp_types.AudioContent): # NOTE: The FastMCP server doesn't support audio content. # See for more details. return messages.BinaryContent( data=base64.b64decode(part.data), media_type=part.mimeType ) # pragma: no cover elif isinstance(part, mcp_types.EmbeddedResource): resource = part.resource return self._get_content(resource) elif isinstance(part, mcp_types.ResourceLink): resource_result: mcp_types.ReadResourceResult = await self._client.read_resource(part.uri) return ( self._get_content(resource_result.contents[0]) if len(resource_result.contents) == 1 else [self._get_content(resource) for resource in resource_result.contents] ) else: assert_never(part) def _get_content( self, resource: mcp_types.TextResourceContents | mcp_types.BlobResourceContents ) -> str | messages.BinaryContent: if isinstance(resource, mcp_types.TextResourceContents): return resource.text elif isinstance(resource, mcp_types.BlobResourceContents): return messages.BinaryContent( data=base64.b64decode(resource.blob), media_type=resource.mimeType or 'application/octet-stream' ) else: assert_never(resource) def __eq__(self, value: object, /) -> bool: return isinstance(value, MCPServer) and self.id == value.id and self.tool_prefix == value.tool_prefix 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 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( # (1)! 'uv', args=['run', 'mcp-run-python', 'stdio'], timeout=10 ) agent = Agent('openai:gpt-4o', toolsets=[server]) ``` 1. See [MCP Run Python](https://github.com/pydantic/mcp-run-python) for more information. """ command: str """The command to run.""" args: Sequence[str] """The arguments to pass to the command.""" env: dict[str, str] | 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`. """ cwd: str | Path | None """The working directory to use when spawning the process.""" # last fields are re-defined from the parent class so they appear as fields tool_prefix: str | None log_level: mcp_types.LoggingLevel | None log_handler: LoggingFnT | None timeout: float read_timeout: float process_tool_call: ProcessToolCallback | None allow_sampling: bool sampling_model: models.Model | None max_retries: int elicitation_callback: ElicitationFnT | None = None def __init__( self, command: str, args: Sequence[str], *, env: dict[str, str] | None = None, cwd: str | Path | None = None, tool_prefix: str | None = None, log_level: mcp_types.LoggingLevel | None = None, log_handler: LoggingFnT | None = None, timeout: float = 5, read_timeout: float = 5 * 60, process_tool_call: ProcessToolCallback | None = None, allow_sampling: bool = True, sampling_model: models.Model | None = None, max_retries: int = 1, elicitation_callback: ElicitationFnT | None = None, id: str | None = None, ): """Build a new MCP server. Args: command: The command to run. args: The arguments to pass to the command. env: The environment variables to set in the subprocess. cwd: The working directory to use when spawning the process. tool_prefix: A prefix to add to all tools that are registered with the server. log_level: The log level to set when connecting to the server, if any. log_handler: A handler for logging messages from the server. timeout: The timeout in seconds to wait for the client to initialize. read_timeout: Maximum time in seconds to wait for new messages before timing out. process_tool_call: Hook to customize tool calling and optionally pass extra metadata. allow_sampling: Whether to allow MCP sampling through this client. sampling_model: The model to use for sampling. max_retries: The maximum number of times to retry a tool call. elicitation_callback: Callback function to handle elicitation requests from the server. id: An optional unique ID for the MCP server. An MCP server needs to have an ID in order to be used in a durable execution environment like Temporal, in which case the ID will be used to identify the server's activities within the workflow. """ self.command = command self.args = args self.env = env self.cwd = cwd super().__init__( tool_prefix, log_level, log_handler, timeout, read_timeout, process_tool_call, allow_sampling, sampling_model, max_retries, elicitation_callback, id=id, ) @classmethod def __get_pydantic_core_schema__(cls, _: Any, __: Any) -> CoreSchema: return core_schema.no_info_after_validator_function( lambda dct: MCPServerStdio(**dct), core_schema.typed_dict_schema( { 'command': core_schema.typed_dict_field(core_schema.str_schema()), 'args': core_schema.typed_dict_field(core_schema.list_schema(core_schema.str_schema())), 'env': core_schema.typed_dict_field( core_schema.dict_schema(core_schema.str_schema(), core_schema.str_schema()), required=False, ), } ), ) @asynccontextmanager async def client_streams( self, ) -> AsyncIterator[ tuple[ MemoryObjectReceiveStream[SessionMessage | Exception], MemoryObjectSendStream[SessionMessage], ] ]: server = StdioServerParameters(command=self.command, args=list(self.args), env=self.env, cwd=self.cwd) async with stdio_client(server=server) as (read_stream, write_stream): yield read_stream, write_stream def __repr__(self) -> str: repr_args = [ f'command={self.command!r}', f'args={self.args!r}', ] if self.id: repr_args.append(f'id={self.id!r}') # pragma: lax no cover return f'{self.__class__.__name__}({", ".join(repr_args)})' def __eq__(self, value: object, /) -> bool: return ( super().__eq__(value) and isinstance(value, MCPServerStdio) and self.command == value.command and self.args == value.args and self.env == value.env and self.cwd == value.cwd ) class _MCPServerHTTP(MCPServer): url: str """The URL of the endpoint on the MCP server.""" headers: dict[str, Any] | None """Optional HTTP headers to be sent with each request to the endpoint. These headers will be passed directly to the underlying `httpx.AsyncClient`. Useful for authentication, custom headers, or other HTTP-specific configurations. !!! note You can either pass `headers` or `http_client`, but not both. See [`MCPServerHTTP.http_client`][pydantic_ai.mcp.MCPServerHTTP.http_client] for more information. """ http_client: httpx.AsyncClient | None """An `httpx.AsyncClient` to use with the endpoint. This client may be configured to use customized connection parameters like self-signed certificates. !!! note You can either pass `headers` or `http_client`, but not both. If you want to use both, you can pass the headers to the `http_client` instead. ```python {py="3.10" test="skip"} import httpx from pydantic_ai.mcp import MCPServerSSE http_client = httpx.AsyncClient(headers={'Authorization': 'Bearer ...'}) server = MCPServerSSE('http://localhost:3001/sse', http_client=http_client) ``` """ # last fields are re-defined from the parent class so they appear as fields tool_prefix: str | None log_level: mcp_types.LoggingLevel | None log_handler: LoggingFnT | None timeout: float read_timeout: float process_tool_call: ProcessToolCallback | None allow_sampling: bool sampling_model: models.Model | None max_retries: int elicitation_callback: ElicitationFnT | None = None def __init__( self, url: str, *, headers: dict[str, str] | None = None, http_client: httpx.AsyncClient | None = None, id: str | None = None, tool_prefix: str | None = None, log_level: mcp_types.LoggingLevel | None = None, log_handler: LoggingFnT | None = None, timeout: float = 5, read_timeout: float | None = None, process_tool_call: ProcessToolCallback | None = None, allow_sampling: bool = True, sampling_model: models.Model | None = None, max_retries: int = 1, elicitation_callback: ElicitationFnT | None = None, **_deprecated_kwargs: Any, ): """Build a new MCP server. Args: url: The URL of the endpoint on the MCP server. headers: Optional HTTP headers to be sent with each request to the endpoint. http_client: An `httpx.AsyncClient` to use with the endpoint. id: An optional unique ID for the MCP server. An MCP server needs to have an ID in order to be used in a durable execution environment like Temporal, in which case the ID will be used to identify the server's activities within the workflow. tool_prefix: A prefix to add to all tools that are registered with the server. log_level: The log level to set when connecting to the server, if any. log_handler: A handler for logging messages from the server. timeout: The timeout in seconds to wait for the client to initialize. read_timeout: Maximum time in seconds to wait for new messages before timing out. process_tool_call: Hook to customize tool calling and optionally pass extra metadata. allow_sampling: Whether to allow MCP sampling through this client. sampling_model: The model to use for sampling. max_retries: The maximum number of times to retry a tool call. elicitation_callback: Callback function to handle elicitation requests from the server. """ if 'sse_read_timeout' in _deprecated_kwargs: if read_timeout is not None: raise TypeError("'read_timeout' and 'sse_read_timeout' cannot be set at the same time.") warnings.warn( "'sse_read_timeout' is deprecated, use 'read_timeout' instead.", DeprecationWarning, stacklevel=2 ) read_timeout = _deprecated_kwargs.pop('sse_read_timeout') _utils.validate_empty_kwargs(_deprecated_kwargs) if read_timeout is None: read_timeout = 5 * 60 self.url = url self.headers = headers self.http_client = http_client super().__init__( tool_prefix, log_level, log_handler, timeout, read_timeout, process_tool_call, allow_sampling, sampling_model, max_retries, elicitation_callback, id=id, ) @property @abstractmethod def _transport_client( self, ) -> Callable[ ..., AbstractAsyncContextManager[ tuple[ MemoryObjectReceiveStream[SessionMessage | Exception], MemoryObjectSendStream[SessionMessage], GetSessionIdCallback, ], ] | AbstractAsyncContextManager[ tuple[ MemoryObjectReceiveStream[SessionMessage | Exception], MemoryObjectSendStream[SessionMessage], ] ], ]: ... @asynccontextmanager async def client_streams( self, ) -> AsyncIterator[ tuple[ MemoryObjectReceiveStream[SessionMessage | Exception], MemoryObjectSendStream[SessionMessage], ] ]: if self.http_client and self.headers: raise ValueError('`http_client` is mutually exclusive with `headers`.') # pragma: no cover transport_client_partial = functools.partial( self._transport_client, url=self.url, timeout=self.timeout, sse_read_timeout=self.read_timeout, ) if self.http_client is not None: # pragma: no cover def httpx_client_factory( headers: dict[str, str] | None = None, timeout: httpx.Timeout | None = None, auth: httpx.Auth | None = None, ) -> httpx.AsyncClient: assert self.http_client is not None return self.http_client async with transport_client_partial(httpx_client_factory=httpx_client_factory) as ( read_stream, write_stream, *_, ): yield read_stream, write_stream else: async with transport_client_partial(headers=self.headers) as (read_stream, write_stream, *_): yield read_stream, write_stream def __repr__(self) -> str: # pragma: no cover repr_args = [ f'url={self.url!r}', ] if self.id: repr_args.append(f'id={self.id!r}') return f'{self.__class__.__name__}({", ".join(repr_args)})' class MCPServerSSE(_MCPServerHTTP): """An MCP server that connects over streamable HTTP connections. This class implements the SSE transport from the MCP specification. See for more information. !!! 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 MCPServerSSE server = MCPServerSSE('http://localhost:3001/sse') agent = Agent('openai:gpt-4o', toolsets=[server]) ``` """ @classmethod def __get_pydantic_core_schema__(cls, _: Any, __: Any) -> CoreSchema: return core_schema.no_info_after_validator_function( lambda dct: MCPServerSSE(**dct), core_schema.typed_dict_schema( { 'url': core_schema.typed_dict_field(core_schema.str_schema()), 'headers': core_schema.typed_dict_field( core_schema.dict_schema(core_schema.str_schema(), core_schema.str_schema()), required=False ), } ), ) @property def _transport_client(self): return sse_client # pragma: no cover def __eq__(self, value: object, /) -> bool: return super().__eq__(value) and isinstance(value, MCPServerSSE) and self.url == value.url @deprecated('The `MCPServerHTTP` class is deprecated, use `MCPServerSSE` instead.') class MCPServerHTTP(MCPServerSSE): """An MCP server that connects over HTTP using the old SSE transport. This class implements the SSE transport from the MCP specification. See for more information. !!! 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" test="skip"} from pydantic_ai import Agent from pydantic_ai.mcp import MCPServerHTTP server = MCPServerHTTP('http://localhost:3001/sse') agent = Agent('openai:gpt-4o', toolsets=[server]) ``` """ class MCPServerStreamableHTTP(_MCPServerHTTP): """An MCP server that connects over HTTP using the Streamable HTTP transport. This class implements the Streamable HTTP transport from the MCP specification. See for more information. !!! 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 MCPServerStreamableHTTP server = MCPServerStreamableHTTP('http://localhost:8000/mcp') agent = Agent('openai:gpt-4o', toolsets=[server]) ``` """ @classmethod def __get_pydantic_core_schema__(cls, _: Any, __: Any) -> CoreSchema: return core_schema.no_info_after_validator_function( lambda dct: MCPServerStreamableHTTP(**dct), core_schema.typed_dict_schema( { 'url': core_schema.typed_dict_field(core_schema.str_schema()), 'headers': core_schema.typed_dict_field( core_schema.dict_schema(core_schema.str_schema(), core_schema.str_schema()), required=False ), } ), ) @property def _transport_client(self): return streamablehttp_client def __eq__(self, value: object, /) -> bool: return super().__eq__(value) and isinstance(value, MCPServerStreamableHTTP) and self.url == value.url ToolResult = ( str | messages.BinaryContent | dict[str, Any] | list[Any] | Sequence[str | messages.BinaryContent | dict[str, Any] | list[Any]] ) """The result type of an MCP tool call.""" CallToolFunc = Callable[[str, dict[str, Any], dict[str, Any] | None], Awaitable[ToolResult]] """A function type that represents a tool call.""" ProcessToolCallback = Callable[ [ RunContext[Any], CallToolFunc, str, dict[str, Any], ], Awaitable[ToolResult], ] """A process tool callback. It accepts a run context, the original tool call function, a tool name, and arguments. Allows wrapping an MCP server tool call to customize it, including adding extra request metadata. """ def _mcp_server_discriminator(value: dict[str, Any]) -> str | None: if 'url' in value: if value['url'].endswith('/sse'): return 'sse' return 'streamable-http' return 'stdio' class MCPServerConfig(BaseModel): """Configuration for MCP servers.""" mcp_servers: Annotated[ dict[ str, Annotated[ Annotated[MCPServerStdio, Tag('stdio')] | Annotated[MCPServerStreamableHTTP, Tag('streamable-http')] | Annotated[MCPServerSSE, Tag('sse')], Discriminator(_mcp_server_discriminator), ], ], Field(alias='mcpServers'), ] def _expand_env_vars(value: Any) -> Any: """Recursively expand environment variables in a JSON structure. Environment variables can be referenced using `${VAR_NAME}` syntax, or `${VAR_NAME:-default}` syntax to provide a default value if the variable is not set. Args: value: The value to expand (can be str, dict, list, or other JSON types). Returns: The value with all environment variables expanded. Raises: ValueError: If an environment variable is not defined and no default value is provided. """ if isinstance(value, str): # Find all environment variable references in the string # Supports both ${VAR_NAME} and ${VAR_NAME:-default} syntax def replace_match(match: re.Match[str]) -> str: var_name = match.group(1) has_default = match.group(2) is not None default_value = match.group(3) if has_default else None # Check if variable exists in environment if var_name in os.environ: return os.environ[var_name] elif has_default: # Use default value if the :- syntax was present (even if empty string) return default_value or '' else: # No default value and variable not set - raise error raise ValueError(f'Environment variable ${{{var_name}}} is not defined') value = _ENV_VAR_PATTERN.sub(replace_match, value) return value elif isinstance(value, dict): return {k: _expand_env_vars(v) for k, v in value.items()} # type: ignore[misc] elif isinstance(value, list): return [_expand_env_vars(item) for item in value] # type: ignore[misc] else: return value def load_mcp_servers(config_path: str | Path) -> list[MCPServerStdio | MCPServerStreamableHTTP | MCPServerSSE]: """Load MCP servers from a configuration file. Environment variables can be referenced in the configuration file using: - `${VAR_NAME}` syntax - expands to the value of VAR_NAME, raises error if not defined - `${VAR_NAME:-default}` syntax - expands to VAR_NAME if set, otherwise uses the default value Args: config_path: The path to the configuration file. Returns: A list of MCP servers. Raises: FileNotFoundError: If the configuration file does not exist. ValidationError: If the configuration file does not match the schema. ValueError: If an environment variable referenced in the configuration is not defined and no default value is provided. """ config_path = Path(config_path) if not config_path.exists(): raise FileNotFoundError(f'Config file {config_path} not found') config_data = pydantic_core.from_json(config_path.read_bytes()) expanded_config_data = _expand_env_vars(config_data) config = MCPServerConfig.model_validate(expanded_config_data) servers: list[MCPServerStdio | MCPServerStreamableHTTP | MCPServerSSE] = [] for name, server in config.mcp_servers.items(): server.id = name server.tool_prefix = name servers.append(server) return servers