gopidesupavan opened a new pull request, #70144:
URL: https://github.com/apache/airflow/pull/70144
Add GoogleCloudToolset to the common.ai provider so agents can call
explicitly allow-listed Google APIs through pydantic-ai tools.
The toolset exposes three tools for agents:
- `list_gcp_methods` to show the allowed Google API methods
- `describe_gcp_method` to inspect parameters and schemas before calling
- `call_gcp` to execute an allowed method and return JSON
## Design
The toolset is intentionally generic rather than service-specific. Instead
of implementing separate wrappers for GCS, BigQuery, Pub/Sub, Cloud Run, and
other Google services, it uses Google Discovery REST metadata to resolve method
names, parameters, request schemas, and response schemas at runtime. This keeps
the provider small while still allowing agents to work with the Google APIs
that publish Discovery documents.
Access is deny-by-default. Dag authors must pass `allowed_methods`, for
example `storage/v1:objects.list` or `bigquery/v2:jobs.query`. Method-level
wildcards are supported, but the API and version must be explicit so an agent
cannot move from an approved stable API to an alpha or beta surface by changing
tool input.
Credentials, impersonation, and project selection come from the Airflow
Google connection through `GoogleBaseHook`. They are not exposed as tool
arguments, so the model can choose only the allowed API method and request
parameters, not which credentials or project to use.
The toolset also adds guardrails around high-risk behavior:
- Credential-returning methods are not matched by wildcards and must be
allow-listed verbatim.
- Media upload/download is rejected.
- Pagination is bounded with `max_pages`.
- Returned payload size is bounded with `max_output_bytes`.
- Project parameters can be pinned to the connection project by default.
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