Enables proactive alerts and interactive Q&A via Google Chat integration.
Provides read-only tools for querying logs, checking service health, and anomaly detection using Google Cloud Logging and BigQuery.
Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type @ followed by the MCP server name and your instructions, e.g., "@LogSentry MCP check payment-service health in the last 30 minutes"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
AI-powered, centralized log-monitoring and Q&A for a fleet of 90+ GCP Java (log4j) microservices.
LogSentry adds three things on top of Google Cloud Logging:
MCP server — read-only tools for log query, service health, and anomaly detection.
AI monitoring agent — scheduled loop that inspects logs via those tools and decides whether to alert.
Google Chat integration — pushes proactive alerts and answers support questions interactively.
Anomaly thresholds are fully parameter-driven (config/thresholds.yaml + env), so tuning needs no code change.
New here? Read SETUP.md — a beginner-to-expert guide for local setup, local testing with examples, and step-by-step Google Cloud deployment.
See BUILD_SPEC.md for the full specification.
TypeScript (Node 20+) · @modelcontextprotocol/sdk · @google-cloud/logging + @google-cloud/bigquery ·
@google-cloud/pubsub · @anthropic-ai/sdk · express · zod + dotenv · vitest + nock ·
Cloud Run + Cloud Scheduler.
Related MCP server: Log Analyzer MCP
npm install
npm run build # tsc strict, must be clean
npm test # all unit + integration (mocked)
npm run test:cov # coverage gate >85% on core modulesCopy .env.example to .env and fill in values for runtime use.
MCP (stdio):
MCP_TRANSPORT=stdio npm run mcp
# another terminal:
npx @modelcontextprotocol/inspector node dist/mcp/server.jsChat bot:
npm run dev
curl -s localhost:8080/health # -> {"status":"ok"}
curl -s -X POST localhost:8080/chat -H 'content-type: application/json' \
-d '{"type":"MESSAGE","message":{"text":"is payment-service healthy?"}}'Agent dry-run (read-only, safe):
DRY_RUN=1 npm run monitor:once # logs the decision, does NOT post to ChatScripts in scripts/ are idempotent and support DRY_RUN=1 (echo instead of execute). Run in order:
Script | Purpose |
| BigQuery dataset + log sink routing |
| Topic + sink for near-real-time agent triggering (optional) |
| Dataset/table + view normalizing the export schema into the |
| Build container, create viewer-only runtime SA, deploy, print URL |
| Cloud Scheduler job hitting |
Full step-by-step deployment, including Google Chat bot registration, is in SETUP.md.
Read-only everywhere — no tool, query, or script writes to production. assertReadOnly guards BigQuery.
Query caps — query_logs hard-caps at MAX_LOGS_PER_QUERY (500) and windows at 24h.
Least-privilege SA — roles/logging.viewer, roles/bigquery.dataViewer, roles/bigquery.jobUser only.
Alert dedup + cooldown — prevents alert storms.
Log tiering — only severity>=WARNING exported to BigQuery; INFO/DEBUG stay in the cheaper default bucket.
Ultra-chatty INFO logs can be sampled at the log4j appender level if volume becomes a problem.
This server cannot be deployed
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