Logging & Tracing Code Generation

Generate structured logging and tracing code tailored to the project's stack. Follow a tiered approach: start with zero dependencies, escalate only when the user needs distributed tracing.

Stack Detection

Before generating code, detect the project's stack and existing logging:

1. Check for package.json (Node.js/TypeScript), requirements.txt/pyproject.toml (Python), go.mod (Go), Cargo.toml (Rust), Gemfile (Ruby)
2. Check for existing logging frameworks:
   • Node.js: winston, pino, bunyan, console
   • Python: logging, loguru, structlog
   • Go: zap, zerolog, logrus, slog
3. Check for existing tracing: OpenTelemetry SDK, Sentry SDK, Datadog agent
4. Detect the application type: API server, CLI tool, web app, worker/queue processor

If existing logging exists, extend it rather than replacing it. If uncertain about the stack, ask the user before generating code.

Tiered Code Generation

Tier 1: Zero-Dependency Structured Logging (Default)

Generate a single logger module using only built-in language features. Output structured JSON to stderr (not stdout, which may be used for data or protocols).

Key requirements:

• Log levels: debug, info, warn, error
• Configurable minimum level via environment variable
• Structured JSON output with: timestamp, level, message, and arbitrary context fields
• Operation name for every log entry
• Duration tracking for async operations

Refer to references/stack-templates.md for full implementation templates per language.

Tier 2: File-Based Logging

When the user needs persistent logs or the debugger's read_logs tool should discover them:

• Write logs to logs/app.jsonl in the project root (JSONL format, append-only)
• Use standard field names: ts (Unix ms), level, msg, op (operation name)
• Add log rotation at 10MB with 2 rotated files maximum
• These locations are auto-discoverable by the debugger's read_logs MCP tool

Tier 3: OpenTelemetry + Free Backends

When the user explicitly requests distributed tracing or mentions OTel/Jaeger/SigNoz:

• Install the OTel SDK for their language
• Create a tracing initialization module with:
  • Graceful degradation when no collector is running
  • Hot-reload safety (prevent duplicate initialization)
  • Smart sampling: 100% in development, 10% in production (100% for errors/slow operations)
• Wrap key operations in spans
• Recommend free backends: Jaeger (local), SigNoz (self-hosted), or Grafana Tempo

Where to Add Logging

Guide the user on strategic placement. Log at these points:

1. Function entry/exit for key operations (API handlers, service methods, data pipelines)
2. External calls — every HTTP request, database query, cache operation, file I/O
3. Error handlers — always log error name, message, stack, and the operation that failed
4. State transitions — authentication changes, workflow steps, queue processing stages
5. Decision points — when code takes a branch based on runtime data (cache hit/miss, feature flag, fallback)

Avoid logging:

• Every iteration of a loop (use summary: "processed 150 items in 230ms")
• Sensitive data (passwords, tokens, PII) — redact before logging
• Redundant information already captured by the framework (e.g., Express request logging middleware)

Log Analysis Guidance

When the user has logs but needs help interpreting them, follow this diagnostic sequence:

1. Start with errors — filter for error/fatal level, read newest first
2. Check timing — look for operations that took >2s or showed sudden duration spikes
3. Look for patterns — repeated errors, cascading failures, periodic spikes
4. Correlate timestamps — align logs across services/components for the same time window
5. Diagnose missing logs — if expected log entries are absent, the code path wasn't reached or the logger isn't configured

Refer to references/log-analysis.md for common error signatures and diagnostic checklists.

Integration with Debugger

Generated logging code integrates with the debugger's read_logs MCP tool when:

• Logs are written to discoverable locations (logs/, *.log, *.jsonl in project root)
• JSONL format uses standard fields (ts, level, msg)
• Error entries include structured error objects (error.name, error.message, error.stack)

After adding logging, tell the user they can read logs using:

Use the debugger read_logs tool with source "project" to view these logs.

Output Format

When generating logging code:

1. Generate the logger module first (single file)
2. Show 2-3 examples of how to use it in existing code
3. Mention the environment variable for log level configuration
4. If Tier 2+, note the log file location and rotation behavior