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    regarding copyright ownership.  The ASF licenses this file
    to you under the Apache License, Version 2.0 (the
    "License"); you may not use this file except in compliance
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    "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
    KIND, either express or implied.  See the License for the
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.. _howto/toolsets:

Toolsets — Airflow Hooks as AI Agent Tools
==========================================

Airflow's 350+ provider hooks already have typed methods, rich docstrings,
and managed credentials. Toolsets expose them as pydantic-ai tools so that
LLM agents can call them during multi-turn reasoning.

Three toolsets are included:

- :class:`~airflow.providers.common.ai.toolsets.hook.HookToolset` — generic
  adapter for any Airflow Hook.
- :class:`~airflow.providers.common.ai.toolsets.sql.SQLToolset` — curated
  4-tool database toolset.
- :class:`~airflow.providers.common.ai.toolsets.mcp.MCPToolset` — connect to
  `MCP servers <https://modelcontextprotocol.io/>`__ configured via Airflow
  connections.

All three implement pydantic-ai's
`AbstractToolset <https://ai.pydantic.dev/toolsets/>`__ interface and can be
passed to any pydantic-ai ``Agent``, including via
:class:`~airflow.providers.common.ai.operators.agent.AgentOperator`.

.. note::

    ``AgentOperator`` accepts **any** ``AbstractToolset`` implementation — not
    just the Airflow-native toolsets above. PydanticAI's own MCP server
    classes (``MCPServerStreamableHTTP``, ``MCPServerSSE``, ``MCPServerStdio``)
    and third-party toolsets work too. The Airflow-native toolsets add
    connection management, secret backend integration, and the connection UI,
    but you are not locked in.


``HookToolset``
---------------

Generic adapter that exposes selected methods of any Airflow Hook as
pydantic-ai tools via introspection. Requires an explicit ``allowed_methods``
list — there is no auto-discovery.

.. code-block:: python

    from airflow.providers.http.hooks.http import HttpHook
    from airflow.providers.common.ai.toolsets.hook import HookToolset

    http_hook = HttpHook(http_conn_id="my_api")

    toolset = HookToolset(
        http_hook,
        allowed_methods=["run"],
        tool_name_prefix="http_",
    )

For each listed method, the introspection engine:

1. Builds a JSON Schema from the method signature (``inspect.signature`` +
   ``get_type_hints``).
2. Extracts the description from the first paragraph of the docstring.
3. Enriches parameter descriptions from Sphinx ``:param:`` or Google
   ``Args:`` blocks.

Parameters
^^^^^^^^^^

- ``hook``: An instantiated Airflow Hook.
- ``allowed_methods``: Method names to expose as tools. Required. Methods
  are validated with ``hasattr`` + ``callable`` at instantiation time.
- ``tool_name_prefix``: Optional prefix prepended to each tool name
  (e.g. ``"s3_"`` produces ``"s3_list_keys"``).


``SQLToolset``
--------------

Curated toolset wrapping
:class:`~airflow.providers.common.sql.hooks.sql.DbApiHook` with four tools:

.. list-table::
   :header-rows: 1
   :widths: 20 50

   * - Tool
     - Description
   * - ``list_tables``
     - Lists available table names (filtered by ``allowed_tables`` if set)
   * - ``get_schema``
     - Returns column names and types for a table
   * - ``query``
     - Executes a SQL query and returns rows as JSON
   * - ``check_query``
     - Validates SQL syntax without executing it

.. code-block:: python

    from airflow.providers.common.ai.toolsets.sql import SQLToolset

    toolset = SQLToolset(
        db_conn_id="postgres_default",
        allowed_tables=["customers", "orders"],
        max_rows=20,
    )

The ``DbApiHook`` is resolved lazily from ``db_conn_id`` on first tool call
via ``BaseHook.get_connection(conn_id).get_hook()``.

Parameters
^^^^^^^^^^

- ``db_conn_id``: Airflow connection ID for the database.
- ``allowed_tables``: Restrict which tables the agent can discover via
  ``list_tables`` and ``get_schema``. ``None`` (default) exposes all tables.
  See :ref:`allowed-tables-limitation` for an important caveat.
- ``schema``: Database schema/namespace for table listing and introspection.
- ``allow_writes``: Allow data-modifying SQL (INSERT, UPDATE, DELETE, etc.).
  Default ``False`` — only SELECT-family statements are permitted.
- ``max_rows``: Maximum rows returned from the ``query`` tool. Default ``50``.

``DataFusionToolset``
---------------------

Curated toolset wrapping
:class:`~airflow.providers.common.sql.datafusion.engine.DataFusionEngine`
with three tools — ``list_tables``, ``get_schema``, and ``query`` — for
querying files on object stores (S3, local filesystem, Iceberg) via Apache DataFusion.

.. list-table::
   :header-rows: 1
   :widths: 20 50

   * - Tool
     - Description
   * - ``list_tables``
     - Lists registered table names
   * - ``get_schema``
     - Returns column names and types for a table (Arrow schema)
   * - ``query``
     - Executes a SQL query and returns rows as JSON

Each :class:`~airflow.providers.common.sql.config.DataSourceConfig` entry
registers a table backed by Parquet, CSV, Avro, or Iceberg data. Multiple
configs can be registered so that SQL queries can join across tables.

.. code-block:: python

    from airflow.providers.common.ai.toolsets.datafusion import DataFusionToolset
    from airflow.providers.common.sql.config import DataSourceConfig

    toolset = DataFusionToolset(
        datasource_configs=[
            DataSourceConfig(
                conn_id="aws_default",
                table_name="sales",
                uri="s3://my-bucket/data/sales/",
                format="parquet",
            ),
            DataSourceConfig(
                conn_id="aws_default",
                table_name="returns",
                uri="s3://my-bucket/data/returns/",
                format="csv",
            ),
        ],
        max_rows=100,
    )

The ``DataFusionEngine`` is created lazily on the first tool call. This
toolset requires the ``datafusion`` extra of
``apache-airflow-providers-common-sql``.

Parameters
^^^^^^^^^^

- ``datasource_configs``: One or more
  :class:`~airflow.providers.common.sql.config.DataSourceConfig` entries.
  Requires ``apache-airflow-providers-common-sql[datafusion]``.
- ``allow_writes``: Allow data-modifying SQL (CREATE TABLE, CREATE VIEW,
  INSERT INTO, etc.). Default ``False`` — only SELECT-family statements are
  permitted. DataFusion on object stores is mostly read-only, but it does
  support DDL for in-memory tables; this guard blocks those by default.
- ``max_rows``: Maximum rows returned from the ``query`` tool. Default ``50``.

``LoggingToolset``
------------------

:class:`~airflow.providers.common.ai.toolsets.logging.LoggingToolset` is a
``WrapperToolset`` that intercepts ``call_tool()`` to log each tool invocation
in real time. ``AgentOperator`` applies it automatically (see
``enable_tool_logging``), but you can also use it directly with any pydantic-ai
``Agent``:

.. code-block:: python

    from airflow.providers.common.ai.toolsets.logging import LoggingToolset
    from airflow.providers.common.ai.toolsets.sql import SQLToolset

    sql_toolset = SQLToolset(db_conn_id="my_db")
    logged_toolset = LoggingToolset(wrapped=sql_toolset, logger=my_logger)

Each tool call produces two INFO log lines (name + timing) and optional
DEBUG-level argument logging. Exceptions are logged and re-raised.


``MCPToolset``
--------------

Connects to an `MCP (Model Context Protocol) <https://modelcontextprotocol.io/>`__
server configured via an Airflow connection. MCP is an open protocol that lets
LLMs interact with external tools and data sources through a standardized
interface.

.. code-block:: python

    from airflow.providers.common.ai.toolsets.mcp import MCPToolset

    toolset = MCPToolset(
        mcp_conn_id="my_mcp_server",
        tool_prefix="weather",
    )

The MCP server is resolved lazily from the Airflow connection on the first
tool call. See :ref:`howto/connection:mcp` for connection configuration.

Requires the ``mcp`` extra: ``pip install "apache-airflow-providers-common-ai[mcp]"``

Parameters
^^^^^^^^^^

- ``mcp_conn_id``: Airflow connection ID for the MCP server.
- ``tool_prefix``: Optional prefix prepended to tool names to avoid
  collisions when using multiple MCP servers (e.g. ``"weather"`` produces
  ``"weather_get_forecast"``).

Using Multiple MCP Servers
^^^^^^^^^^^^^^^^^^^^^^^^^^

.. code-block:: python

    AgentOperator(
        task_id="multi_mcp",
        prompt="Get the weather in London and run a calculation",
        llm_conn_id="pydanticai_default",
        toolsets=[
            MCPToolset(mcp_conn_id="weather_mcp", tool_prefix="weather"),
            MCPToolset(mcp_conn_id="code_runner_mcp", tool_prefix="code"),
        ],
    )

Direct PydanticAI MCP Servers
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

For prototyping or when you want full PydanticAI control, you can pass MCP
server instances directly — no Airflow connection needed:

.. code-block:: python

    from pydantic_ai.mcp import MCPServerStreamableHTTP, MCPServerStdio

    AgentOperator(
        task_id="direct_mcp",
        prompt="What tools are available?",
        llm_conn_id="pydanticai_default",
        toolsets=[
            MCPServerStreamableHTTP("http://localhost:3001/mcp"),
            MCPServerStdio("uvx", args=["mcp-run-python"]),
        ],
    )

This works because PydanticAI's MCP server classes implement
``AbstractToolset``. The tradeoff: URLs and credentials are hardcoded in DAG
code instead of being managed through Airflow connections and secret backends.


Security
--------

LLM agents call tools based on natural-language reasoning. This makes them
powerful but introduces risks that don't exist with deterministic operators.

Defense Layers
^^^^^^^^^^^^^^

No single layer is sufficient — they work together.

.. list-table::
   :header-rows: 1
   :widths: 20 40 40

   * - Layer
     - What it does
     - What it does NOT do
   * - **Airflow Connections**
     - Credentials are stored in Airflow's secret backend, never in DAG code.
       The LLM agent cannot see API keys or database passwords.
     - Does not prevent the agent from using the connection to access data
       the connection has access to.
   * - **HookToolset: explicit allow-list**
     - Only methods listed in ``allowed_methods`` are exposed as tools.
       Auto-discovery is not supported. Methods are validated at DAG parse
       time.
     - Does not restrict what arguments the agent passes to allowed methods.
   * - **SQLToolset: read-only by default**
     - ``allow_writes=False`` (default) validates every SQL query through
       ``validate_sql()`` and rejects INSERT, UPDATE, DELETE, DROP, etc.
     - Does not prevent the agent from reading sensitive data that the
       database user has SELECT access to.
   * - **DataFusionToolset: read-only by default**
     - ``allow_writes=False`` (default) validates every SQL query through
       ``validate_sql()`` and rejects CREATE TABLE, CREATE VIEW, INSERT
       INTO, and other non-SELECT statements.
     - Does not prevent the agent from reading any registered data source.
   * - **SQLToolset: allowed_tables**
     - Restricts which tables appear in ``list_tables`` and ``get_schema``
       responses, limiting the agent's knowledge of the schema.
     - Does **not** validate table references in SQL queries. The agent can
       still query unlisted tables if it guesses the name. See
       :ref:`allowed-tables-limitation` below.
   * - **SQLToolset: max_rows**
     - Truncates query results to ``max_rows`` (default 50), preventing the
       agent from pulling entire tables into context.
     - Does not limit the number of queries the agent can make.
   * - **pydantic-ai: tool call budget**
     - pydantic-ai's ``max_result_retries`` and ``model_settings`` control
       how many tool-call rounds the agent can make before stopping.
     - Requires explicit configuration — the default allows many rounds.


.. _allowed-tables-limitation:

The ``allowed_tables`` Limitation
"""""""""""""""""""""""""""""""""

``allowed_tables`` is a **metadata filter**, not an access control mechanism.
It hides table names from ``list_tables`` and blocks ``get_schema`` for
unlisted tables, but does not parse SQL queries to validate table references.

An LLM can craft ``SELECT * FROM secrets`` even when
``allowed_tables=["orders"]``. Parsing SQL for table references (including
CTEs, subqueries, aliases, and vendor-specific syntax) is complex and
error-prone; we chose not to provide a false sense of security.

For query-level restrictions, use database permissions:

.. code-block:: sql

    -- Create a read-only role with access to specific tables only
    CREATE ROLE airflow_agent_reader;
    GRANT SELECT ON orders, customers TO airflow_agent_reader;
    -- Use this role's credentials in the Airflow connection

The Airflow connection should use a database user with the minimum privileges
required.


HookToolset Guidelines
""""""""""""""""""""""

- List only the methods the agent needs. Never expose ``run()`` or
  ``get_connection()`` — these give broad access.
- Prefer read-only methods (``list_*``, ``get_*``, ``describe_*``).
- The agent controls arguments. If a method accepts a ``path`` parameter,
  the agent can pass any path the hook has access to.

.. code-block:: python

    # Good: expose only list and read
    HookToolset(
        s3_hook,
        allowed_methods=["list_keys", "read_key"],
        tool_name_prefix="s3_",
    )

    # Bad: exposes delete and write operations
    HookToolset(
        s3_hook,
        allowed_methods=["list_keys", "read_key", "delete_object", "load_string"],
    )


Recommended Configuration
"""""""""""""""""""""""""

**Read-only analytics** (the most common pattern):

.. code-block:: python

    SQLToolset(
        db_conn_id="analytics_readonly",  # Connection with SELECT-only grants
        allowed_tables=["orders", "customers"],  # Hide other tables from agent
        allow_writes=False,  # Default — validates SQL
        max_rows=50,  # Default — truncate large results
    )

**Agents that need to modify data** (use with caution):

.. code-block:: python

    SQLToolset(
        db_conn_id="app_db",
        allowed_tables=["user_preferences"],
        allow_writes=True,  # Disables SQL validation — agent can INSERT/UPDATE
        max_rows=100,
    )


Production Checklist
""""""""""""""""""""

Before deploying an agent task to production:

1. **Connection credentials**: Use Airflow's secret backend. Never hardcode
   API keys in DAG files.
2. **Database permissions**: Create a dedicated database user with minimum
   required grants. Don't reuse the admin connection.
3. **Tool allow-list**: Review ``allowed_methods`` / ``allowed_tables``. The
   agent can call any exposed tool with any arguments.
4. **Read-only default**: Keep ``allow_writes=False`` unless the task
   specifically requires writes.
5. **Row limits**: Set ``max_rows`` appropriate to the use case. Large
   result sets consume LLM context and increase cost.
6. **Model budget**: Configure pydantic-ai's ``model_settings`` (e.g.
   ``max_tokens``) and ``retries`` to bound cost and prevent runaway loops.
7. **System prompt**: Include safety instructions in ``system_prompt`` (e.g.
   "Only query tables related to the question. Never modify data.").
8. **Prompt injection**: Be cautious when the prompt includes untrusted data
   (user input, external API responses, upstream XCom). Consider sanitizing
   inputs before passing them to the agent.
