Responded16 / 22 checks passed
Remote MCP server to run your Atako AI agents: chat, projects, files, integrations and channels.
- Passed: Handshake
- Passed: Tools listed
- Passed: Updated recently
Median latency 132 ms · Last checked
Independent monitoring
Health & checks
Responded
The server completed its last measured handshake. This is an observation, not a guarantee.
Last checked| Test area | Passed | Failed | Unmeasured |
|---|---|---|---|
ConnectionCan a client connect and discover tools? | 5 | 0 | 0 |
Reliability & speedDoes it respond consistently and quickly? | 0 | 0 | 4 |
Access & authenticationWhat is required to use this server? | 1 | 0 | 2 |
Tool qualityAre the tool definitions usable by an agent? | 4 | 0 | 0 |
Publisher identityCan the publisher be traced to its source? | 1 | 3 | 2 |
Unmeasured means there is not enough evidence to decide. How these checks work
Inspect individual checks 22 recorded
Core
- Pass
- Pass
- Pass
- Pass
- Pass
Performance
- Not measured
- Not measured
- Not measured
- Not measured
Access
- Pass
- Not measured
- Not measured
Tools
- Pass
- Pass
- Pass
- Pass
Publisher
- Pass
- Fail
- Fail
- Not measured
- Fail
- Not measured
Start using Google Cloud Run
Connect to this server
Choose your client, copy the configuration, then enable the server in your client’s MCP settings.
Run this command in your terminal, then restart Claude Code.
Claude Code
claude mcp add --transport http google-cloud-run https://run.googleapis.com/mcpCapabilities
Tools 5
deploy_service_from_archive
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).
Discovereddeploy_service_from_file_contents
Deploys a Cloud Run service directly from local source files. This method is suitable for scripting languages like Python and Node.js, of which the source code can be embedded in the request. This is ideal for quick tests and development feedback loops. You must include all necessary dependencies within the source files because it skips the build step for faster deployment. **Key Requirements:** 1. source_code: Should set to sourceCode.inlinedSource.sources with array of source files, each having `filename` and `content`. 2. Size limit: you are subject to total request size limit of 50MiB.
Discovereddeploy_service_from_image
Deploy a container image from Artifact Registry or Docker Hub as a Cloud Run service.
Discoveredget_service
Get info about a Cloud Run service, such as its URI and whether the deploy succeeded.
Discoveredlist_services
List Cloud Run services in a given Google Cloud project and region.
DiscoveredRecent measurements
Probe history
| Checked | Result | Response | Tools |
|---|---|---|---|
| Connected | 248 ms | 5 |