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Source code for airflow.providers.common.ai.hooks.mcp

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# to you under the Apache License, Version 2.0 (the
# "License"); you may not use this file except in compliance
# with the License.  You may obtain a copy of the License at
#
#   http://www.apache.org/licenses/LICENSE-2.0
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# software distributed under the License is distributed on an
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from __future__ import annotations

from typing import TYPE_CHECKING, Any

from airflow.providers.common.compat.sdk import BaseHook

if TYPE_CHECKING:
    from collections.abc import Callable

    from airflow.sdk.execution_time.secrets_masker import mask_secret
else:
    try:
        from airflow.sdk.log import mask_secret
    except ImportError:
        try:
            from airflow.sdk.execution_time.secrets_masker import mask_secret
        except ImportError:
            from airflow.utils.log.secrets_masker import mask_secret


[docs] class MCPHook(BaseHook): """ Hook for connecting to MCP (Model Context Protocol) servers. Manages connection configuration for MCP servers. Supports three transport types: HTTP (Streamable HTTP), SSE, and stdio. Connection fields: - **host**: Server URL for HTTP/SSE transports (e.g. ``http://localhost:3001/mcp``) - **password**: Auth token (optional) - **Extra.transport**: Transport type — ``http`` (default), ``sse``, or ``stdio`` - **Extra.command**: Command to run for stdio transport (e.g. ``uvx``) - **Extra.args**: Command arguments for stdio transport (e.g. ``["mcp-run-python"]``) - **Extra.env**: Environment variables for the stdio subprocess (e.g. ``{"API_KEY": "..."}``). Ignored for HTTP/SSE. - **Extra.timeout**: Connection init timeout in seconds for stdio (default: 10) For HTTP/SSE transports the ``Authorization`` header is, by default, a static ``Bearer`` token taken from the connection ``password``. Endpoints that require a freshly minted or short-lived token (e.g. a Snowflake managed MCP server authenticated with a key-pair JWT, OAuth/refresh tokens, Workload Identity Federation, or GitHub App installation tokens) can pass a ``token_provider`` callable instead. It is invoked once, the first time this hook establishes a connection, and its return value is used as the bearer token, so a fresh token is minted without storing a long-lived secret in the connection. For the ``stdio`` transport, the subprocess environment is, by default, the static ``Extra.env`` mapping -- a fine place for a value that genuinely belongs to this connection. When the credential has no stable static form to store here at all -- it lives in a different connection (e.g. a Splunk credential a Splunk connection already manages), or is minted fresh per call (e.g. a Vault lease) -- pass an ``env_provider`` callable instead. Its return value is merged over ``Extra.env`` (``env_provider`` keys win on conflicts), invoked once, the first time this hook establishes a connection. :param mcp_conn_id: Airflow connection ID for the MCP server. :param tool_prefix: Optional prefix prepended to tool names (e.g. ``"weather"`` → ``"weather_get_forecast"``). :param token_provider: Optional zero-argument callable returning a bearer token string. When set, it overrides the connection ``password`` for the ``Authorization`` header on HTTP/SSE transports. Called once, the first time this hook establishes a connection (the result is then cached for the hook's lifetime). Ignored for the ``stdio`` transport. :param env_provider: Optional zero-argument callable returning a ``dict[str, str]`` of environment variables for the ``stdio`` subprocess. Merged over ``Extra.env`` (``env_provider`` wins on key conflicts). Called once, the first time this hook establishes a connection (the result is then cached for the hook's lifetime). Ignored for the ``http``/``sse`` transports. """
[docs] conn_name_attr = "mcp_conn_id"
[docs] default_conn_name = "mcp_default"
[docs] conn_type = "mcp"
[docs] hook_name = "MCP Server"
def __init__( self, mcp_conn_id: str = default_conn_name, tool_prefix: str | None = None, *, token_provider: Callable[[], str] | None = None, env_provider: Callable[[], dict[str, str]] | None = None, **kwargs, ) -> None: super().__init__(**kwargs)
[docs] self.mcp_conn_id = mcp_conn_id
[docs] self.tool_prefix = tool_prefix
[docs] self.token_provider = token_provider
[docs] self.env_provider = env_provider
self._server: Any = None @staticmethod
[docs] def get_ui_field_behaviour() -> dict[str, Any]: """Return custom field behaviour for the Airflow connection form.""" return { "hidden_fields": ["schema", "port", "login"], "relabeling": {"password": "Auth Token"}, "placeholders": { "host": "http://localhost:3001/mcp (for HTTP/SSE transport)", }, }
def _auth_headers(self, conn: Any) -> dict[str, str] | None: """ Build the ``Authorization`` header for HTTP/SSE transports. Prefers ``token_provider`` (minted per connection) over the static connection ``password``. Returns ``None`` when neither yields a token. """ if self.token_provider is not None: token = self.token_provider() if not isinstance(token, str) or not token: raise ValueError( f"token_provider for connection {self.mcp_conn_id!r} must return a non-empty " f"string token, got {type(token).__name__}." ) # The static connection password is masked when the connection is # fetched; mask the minted token too so it never leaks into task logs. mask_secret(token) return {"Authorization": f"Bearer {token}"} if conn.password: return {"Authorization": f"Bearer {conn.password}"} return None def _validate_env_dict(self, env: dict[Any, Any], source: str) -> None: """Raise if ``env`` (from ``source``, for error messages) has non-string keys/values.""" for key, value in env.items(): if not isinstance(key, str) or not key: raise ValueError( f"{source} for connection {self.mcp_conn_id!r} must have non-empty string keys, " f"got {type(key).__name__}." ) if not isinstance(value, str) or not value: raise ValueError( f"{source} for connection {self.mcp_conn_id!r} must have non-empty string values, " f"got {type(value).__name__} for key {key!r}." ) def _stdio_env(self, extra: dict[str, Any]) -> dict[str, str] | None: """ Build the subprocess environment for the ``stdio`` transport. Merges the static ``Extra.env`` mapping with ``env_provider`` (minted per connection), with ``env_provider`` keys taking precedence. Returns ``None`` when neither yields anything, in which case the MCP stdio client falls back to a small allowlist of inherited variables (see the ``mcp`` package's ``get_default_environment()``), not the full parent process environment. """ extra_env = extra.get("env") if extra_env is None: extra_env = {} if not isinstance(extra_env, dict): raise ValueError( f"'env' in extra for connection {self.mcp_conn_id!r} must be an object of " f"string keys/values, got {type(extra_env).__name__}." ) self._validate_env_dict(extra_env, "'env' in extra") env: dict[str, str] = dict(extra_env) if self.env_provider is not None: provided = self.env_provider() if not isinstance(provided, dict): raise ValueError( f"env_provider for connection {self.mcp_conn_id!r} must return a dict[str, str], " f"got {type(provided).__name__}." ) self._validate_env_dict(provided, "env_provider") # The static connection extra is masked when the connection is fetched; # mask minted values too so they never leak into task logs. for value in provided.values(): mask_secret(value) env.update(provided) return env or None
[docs] def get_conn(self) -> Any: """ Return a configured PydanticAI MCP toolset instance. Builds a :class:`~pydantic_ai.mcp.MCPToolset` over the FastMCP transport matching the transport type in the connection's extra field: - ``http`` (default): ``fastmcp.client.transports.StreamableHttpTransport`` - ``sse``: ``fastmcp.client.transports.SSETransport`` - ``stdio``: ``fastmcp.client.transports.StdioTransport`` When ``tool_prefix`` is set the toolset is wrapped via :meth:`~pydantic_ai.toolsets.abstract.AbstractToolset.prefixed`, so a prefix of ``"weather"`` yields tool names like ``weather_get_forecast``. The result is cached for the lifetime of this hook instance. """ if self._server is not None: return self._server try: from fastmcp.client.transports import SSETransport, StdioTransport, StreamableHttpTransport from pydantic_ai.mcp import MCPToolset except ImportError: raise ImportError( 'MCP support requires the `mcp` package. Install it with: pip install "pydantic-ai-slim[mcp]"' ) conn = self.get_connection(self.mcp_conn_id) extra = conn.extra_dejson transport = extra.get("transport", "http") if transport == "http": if not conn.host: raise ValueError(f"Connection {self.mcp_conn_id!r} requires a host URL for HTTP transport.") toolset = MCPToolset(StreamableHttpTransport(conn.host, headers=self._auth_headers(conn))) elif transport == "sse": if not conn.host: raise ValueError(f"Connection {self.mcp_conn_id!r} requires a host URL for SSE transport.") toolset = MCPToolset(SSETransport(conn.host, headers=self._auth_headers(conn))) elif transport == "stdio": command = extra.get("command") if not command: raise ValueError( f"Connection {self.mcp_conn_id!r} requires 'command' in extra for stdio transport." ) args = extra.get("args", []) if isinstance(args, str): args = [args] timeout = extra.get("timeout", 10) toolset = MCPToolset( StdioTransport(command=command, args=args, env=self._stdio_env(extra)), init_timeout=timeout, ) else: raise ValueError( f"Unknown transport {transport!r} in connection {self.mcp_conn_id!r}. " "Supported: 'http', 'sse', 'stdio'." ) self._server = toolset.prefixed(self.tool_prefix) if self.tool_prefix else toolset return self._server
[docs] def test_connection(self) -> tuple[bool, str]: """ Test connection by verifying configuration is valid. Validates that the connection has the required fields for the configured transport type. Does NOT connect to the MCP server — that requires an async context manager. """ try: self.get_conn() return True, "MCP server configuration is valid." except Exception as e: return False, str(e)

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