List Cloud Run services in a given Google Cloud project and region.

List BigQuery dataset IDs in a Google Cloud project.

Deploy a Cloud Run service directly from a self-contained source code archive (.tar.gz), skipping the container image build step for faster deployment. The archive must include all dependencies: - For compiled languages (Go, Java), include pre-compiled binaries. - For scripting languages (Python, Node.js), include pre-installed libraries (e.g., vendor/, node_modules/). Deployment steps: 1. Package source code and dependencies into a .tar.gz archive (max 250MiB). It's recommended to create archive from the root of the application's source directory. 2. Upload the archive to a Google Cloud Storage bucket, preferably in the same region as the service. 3. Deploy to Cloud Run using this tool, specifying: - source_code: Google Cloud Storage object path to the archive (e.g., gs://bucket/object). - command: Command to start the application. - base_image_uri: Base image for the container (e.g., go124, nodejs24, python314). See https://docs.cloud.google.com/run/docs/configuring/services/runtime-base-images for options. The runtime picked should match the local environment. - args: (Optional) Arguments for the command. - env: (Optional) Environment variables (e.g., name: `PYTHONPATH`, value: `./vendor`). - ports: (Optional) Container ports to expose (defaults to 8080).

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