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How Claude Code Could Leverage Local Observability Infrastructure

Published on December 24, 2025 ยท Updated December 24, 2025

How Claude Code Could Leverage Local Observability Infrastructure

Slug: claude-code-observability-integration
Excerpt: With our containerized dev environment now including Grafana, Loki, Prometheus, Jaeger, and SonarQube, we explore how AI coding assistants like Claude Code could use this infrastructure to make smarter decisions about what to build and how.

We recently completed containerizing our development environment and adding a comprehensive observability stack. What started as a way to monitor our own application has sparked an interesting question: What if Claude Code could access all this data?

Our Current Stack

Our local development environment now includes:

  • Grafana (port 3000) - Unified dashboards for all observability data
  • Prometheus (port 9090) - Metrics collection and time-series database
  • Loki (port 3100) - Log aggregation and querying
  • Jaeger (port 16686) - Distributed tracing
  • SonarQube (port 9000) - Code quality and technical debt analysis

All of this runs locally via Docker Compose, giving us production-grade observability for our development workflow.

The Opportunity

Right now, when we ask Claude Code to help with our codebase, it operates primarily on static information: source files, documentation, and our conversation history. But there's a wealth of runtime and quality data sitting in our observability stack that could dramatically improve AI-assisted development.

1. Prioritizing What to Build

Imagine Claude Code having access to:

  • Error rates by endpoint (Prometheus/Grafana) - "The /api/tour/translate/ endpoint has a 3% error rate, highest in the app"
  • Slow query patterns (Jaeger traces) - "The property list view averages 800ms, with 60% of time spent in database queries"
  • User behavior patterns (application metrics) - "The Spanish translation feature is used 5x more than Chinese"

With this data, Claude could proactively suggest: "Based on your error rates, I'd prioritize fixing the translation endpoint. Would you like me to investigate the root cause?"

2. Smarter Implementation Decisions

Access to historical data could inform how Claude approaches problems:

  • SonarQube trends - "Technical debt has increased 15% in the last month, primarily in the views.py file. Should we refactor before adding new features?"
  • Performance baselines - "Similar endpoints respond in 50ms. This implementation might need optimization."
  • Test coverage gaps - "The billing module has 45% coverage vs 85% elsewhere. We should add tests."

3. Quality Control During Development

Real-time feedback during implementation:

  • Before committing - "Running SonarQube analysis... found 2 new code smells and 1 security hotspot"
  • After deployment - "Monitoring the staging environment... error rate stable, no new exceptions in Loki"
  • Regression detection - "Response times increased 40% after the last change. Here's the trace showing the bottleneck."

Technical Feasibility

All of these tools expose APIs that could be queried:

# Prometheus - Query metrics
curl 'http://localhost:9090/api/v1/query?query=http_requests_total'

# Loki - Search logs
curl 'http://localhost:3100/loki/api/v1/query_range?query={app="tourinator"}'

# Jaeger - Get traces
curl 'http://localhost:16686/api/traces?service=tourinator'

# SonarQube - Get issues
curl 'http://localhost:9000/api/issues/search?componentKeys=tourinator'

Claude Code already supports MCP (Model Context Protocol) servers, which could wrap these APIs and provide structured access to observability data.

Privacy and Security Considerations

For this to work responsibly:

  • Local only - All data stays on the development machine
  • Opt-in - Developers choose what data to expose
  • No sensitive data - Metrics and traces, not actual user content
  • Clear boundaries - Claude reports what it sees, developers decide what to do

What We'd Want

If we could design this integration, here's what would be most valuable:

  1. "Analyze my app's health" - Summary of error rates, slow endpoints, recent exceptions
  2. "What's causing this bug?" - Trace analysis when investigating issues
  3. "Review my code quality" - SonarQube summary before releases
  4. "What should I work on next?" - Data-driven prioritization suggestions
  5. "Did my change break anything?" - Post-deployment monitoring

Conclusion

We've built the infrastructure. The data is there. The APIs are accessible. The missing piece is connecting AI coding assistants to this wealth of runtime and quality information.

This isn't about replacing developer judgment - it's about giving AI assistants the same context that experienced developers use when making decisions. When a senior engineer says "we should fix that flaky endpoint first," they're drawing on their knowledge of error rates, user impact, and system behavior. Why shouldn't Claude have access to the same information?

We're excited to see where this goes. In the meantime, we'll keep improving our observability stack and dreaming about a future where make sonar-scan automatically informs Claude's next suggestion.

What do you think? Would you want your AI coding assistant to have access to your observability data? Let us know.

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