srtux mcp

WHAT IT CONNECTS

What srtux mcp gives an AI Agent

• **Natural language to LQL translation** using Vertex AI Gemini 2.5
• **Flexible log querying**: filter on monitored resource, log name, severity, time, and more
• **REST API** for easy integration
• **Ready for deployment** on Google Cloud Run or GKE
• OpenAPI/Swagger docs available at `/docs` and `/openapi.json` when running.
• Both endpoints are also discoverable as MCP tools for agent frameworks (Smithery, Claude Desktop, etc).
• Example test script: `test_main.py` (see repo)
• Standard Python ignores included (see repo)
• **Build the Docker image:**
• **Deploy to Cloud Run:**
• **Set Environment Variables:**
• In the Cloud Run deployment UI or with the `--set-env-vars` flag, provide:
• `VERTEX_PROJECT=your-gcp-project-id`
• `VERTEX_LOCATION=us-central1` (or your region)
• **Credentials:**
• Prefer using the Cloud Run service account with the right IAM roles (Logging Viewer, Vertex AI User).
• You usually do NOT need to set `GOOGLE_APPLICATION_CREDENTIALS` on Cloud Run unless using a non-default service account key.
• **IAM Permissions:**
• Ensure the Cloud Run service account has:
• `roles/logging.viewer`
• `roles/aiplatform.user`
• **Accessing the Service:**
• After deployment, Cloud Run will provide a service URL (e.g., `https://mcp-logging-server-xxxxxx.a.run.app`).
• Use this as your `$MCP_BASE_URL` in API requests.
• **Create a Service Account:**
• Go to the [Google Cloud Console → IAM & Admin → Service Accounts](https://console.cloud.google.com/iam-admin/serviceaccounts).
• Select your project.
• Create or select a service account with permissions: _Logging Viewer_ and _Vertex AI User_.
• **Create and Download a Key:**
• In the Service Account, click "Manage keys" → "Add key" → "Create new key" (choose JSON).
• Download the JSON key file to your computer.
• **Set the Environment Variable:**
• In your terminal, set the environment variable to the path of your downloaded key:
• Replace `/path/to/your/service-account-key.json` with the actual path.
• **(Optional) Set Project and Location:**
• You may also need:
• **Verify Authentication:**
• Run a simple `gcloud` or Python client call to ensure authentication is working.
• If you see `DefaultCredentialsError`, check your environment variable and file path.
• Python 3.9+
• Google Cloud project with Logging and Vertex AI APIs enabled
• Service account with permissions for Logging Viewer and Vertex AI User
• Set environment variables:
• `VERTEX_PROJECT`: Your GCP project ID
• `VERTEX_LOCATION`: Vertex AI region (default: `us-central1`)
• `GOOGLE_APPLICATION_CREDENTIALS`: Path to your service account JSON key file
• Show all logs from Kubernetes clusters
• Show error logs from Compute Engine and AWS EC2 instances
• Find Admin Activity audit logs for project my-project
• Find logs containing the word unicorn
• Find logs with both unicorn and phoenix
• Find logs where textPayload contains both unicorn and phoenix
• Find logs where textPayload contains the phrase 'unicorn phoenix'
• Show logs from yesterday for Cloud Run service 'my-service'
• Show logs from the last 30 minutes
• Show logs for logName containing request_log in GKE
• Show logs where pod_name matches foo or bar using regex
• Show logs for Compute Engine where severity is WARNING or higher
• Show logs for Cloud SQL instances in us-central1
• Show logs for Pub/Sub topics containing 'payments'
• Show logs for log entries between two timestamps
• Show logs where jsonPayload.message matches regex 'foo.*bar'
• Show logs where labels.env is not prod