Story

I built a self-hosted observability server that exposes production data as MCP tools. Instead of switching between dashboards and your editor, you connect it to Claude Code, Cursor, or any MCP client and query your logs, database, and server metrics through natural language.

What it covers:

- Log ingestion with full-text search (SQLite FTS5), filters by service, level, trace ID, exception class, metadata - Read-only Postgres introspection — query stats from pg_stat_statements, index analysis, lock chains, bloat estimates, replication lag. All queries validated SELECT-only via SQL AST parsing (pg_query) - Sentry-style error grouping by fingerprint with user impact analysis - User analytics — session journeys, conversion funnels, path analysis, top endpoints - VM monitoring — CPU, memory, disk, network via gopsutil - Rule-based threshold watches with auto-resolve

The AI assistant can also take actions: resolve errors, create watches, set up health checks, kill slow queries, and save persistent notes across sessions.

Tools return suggested_tools with pre-filled arguments, so the assistant chains through investigations without prompt engineering.

Stack: Go, SQLite (WAL + FTS5), Chi, HTMX. Single binary, no external dependencies. Runs on a $4 VPS.

Client libraries: Ruby gem for Rails (auto-captures SQL, N+1s, view renders, ActiveJob, PII redaction) and a 3.1KB browser JS client for frontend error tracking.

https://github.com/adham90/opentrace