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,...
Risk signalsAdmin/system-level operation
Part of the Google Cloud Run server.
Free to start. No card required.
AI agents invoke deploy_service_from_archive to trigger processes or run actions in Google Cloud Run. Execute operations can have side effects beyond the immediate call -- triggering builds, sending notifications, or starting workflows. Rate limits and argument validation are essential to prevent runaway execution.
deploy_service_from_archive can trigger processes with real-world consequences. An uncontrolled agent might start dozens of builds, send mass notifications, or kick off expensive compute jobs. PolicyLayer enforces rate limits and validates arguments to keep execution within safe bounds.
Execute tools trigger processes. Rate-limit and validate arguments to prevent unintended side effects.
{
"version": "1",
"default": "deny",
"tools": {
"deploy_service_from_archive": {
"limits": [
{
"counter": "deploy_service_from_archive_rate",
"window": "minute",
"max": 10,
"scope": "grant"
}
]
}
}
}See the full Google Cloud Run policy for all 5 tools.
These attack patterns abuse exactly the kind of access deploy_service_from_archive gives an agent. Each links to the full case and the policy that stops it:
Other execute tools across the catalogue. The same approach applies to each: rate-limit and validate the arguments.
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).. It is categorised as a Execute tool in the Google Cloud Run MCP Server, which means it can trigger actions or run processes. Use rate limits and argument validation.
Register the Google Cloud Run MCP server in PolicyLayer and add a rule for deploy_service_from_archive: allow, deny, rate-limit, or require approval. Point your MCP client at the PolicyLayer proxy URL and the rule is enforced on every call, before it reaches Google Cloud Run. Nothing to install.
deploy_service_from_archive is a Execute tool with high risk. Execute tools should be rate-limited and have argument validation enabled.
Yes. Add a rate_limit block to the deploy_service_from_archive rule in your PolicyLayer policy. For example, setting max: 10 and window: 60 limits the tool to 10 calls per minute. Rate limits are tracked per agent session and reset automatically.
Set action: deny in the PolicyLayer policy for deploy_service_from_archive. The AI agent will receive a policy violation error and cannot call the tool. You can also include a reason field to explain why the tool is blocked.
deploy_service_from_archive is provided by the Google Cloud Run MCP server (https://run.googleapis.com/mcp). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
Deterministic rules across all 5 Google Cloud Run tools. Per-identity grants. Full audit log. Live in minutes. Nothing to install.
Free to start. No card required.
4,600+ MCP servers and 31,000+ tools scanned and risk-classified.