LogSentry

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:

1. MCP server — read-only tools for log query, service health, and anomaly detection.
2. AI monitoring agent — scheduled loop that inspects logs via those tools and decides whether to alert.
3. 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.

Tech stack

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.

Quick start (local, no cloud needed)

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 modules

Copy .env.example to .env and fill in values for runtime use.

Local smoke tests

MCP (stdio):

MCP_TRANSPORT=stdio npm run mcp
# another terminal:
npx @modelcontextprotocol/inspector node dist/mcp/server.js

Chat 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 Chat

Deployment

Scripts in scripts/ are idempotent and support DRY_RUN=1 (echo instead of execute). Run in order:

Script	Purpose
01-setup-logging-sink.sh	BigQuery dataset + log sink routing severity>=WARNING to BigQuery (cost lever)
02-setup-pubsub.sh	Topic + sink for near-real-time agent triggering (optional)
03-setup-bigquery.sh	Dataset/table + view normalizing the export schema into the LogEntry shape
04-deploy-cloudrun.sh	Build container, create viewer-only runtime SA, deploy, print URL
05-setup-scheduler.sh	Cloud Scheduler job hitting POST /monitor every MONITOR_INTERVAL_MINUTES (OIDC)

Full step-by-step deployment, including Google Chat bot registration, is in SETUP.md.

Safety guardrails

• 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.