Fixes critical issue where Docker and cloud deployments generated new
anonymous user IDs on every container recreation, causing 100-200x
inflation in unique user counts.
Changes:
- Use host's boot_id for stable identification across container updates
- Auto-detect Docker (IS_DOCKER=true) and 8 cloud platforms
- Defensive fallback chain: boot_id → combined signals → generic ID
- Zero configuration required
Impact:
- Resolves ~1000x/month inflation in stdio mode
- Resolves ~180x/month inflation in HTTP mode (6 releases/day)
- Improves telemetry accuracy: 3,996 apparent users → ~2,400-2,800 actual
Testing:
- 18 new unit tests for boot_id functionality
- 16 new integration tests for Docker/cloud detection
- All 60 telemetry tests passing (100%)
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude <noreply@anthropic.com>
Major improvements to telemetry system addressing code review findings:
Architecture & Modularization:
- Split 636-line TelemetryManager into 7 focused modules
- Separated concerns: event tracking, batch processing, validation, rate limiting
- Lazy initialization pattern to avoid early singleton creation
- Clean separation of responsibilities
Security & Privacy:
- Added comprehensive input validation with Zod schemas
- Sanitization of sensitive data (URLs, API keys, emails)
- Expanded sensitive key detection patterns (25+ patterns)
- Row Level Security on Supabase backend
- Added data deletion contact info (romuald@n8n-mcp.com)
Performance & Reliability:
- Sliding window rate limiter (100 events/minute)
- Circuit breaker pattern for network failures
- Dead letter queue for failed events
- Exponential backoff with jitter for retries
- Performance monitoring with overhead tracking (<5%)
- Memory-safe array limits in rate limiter
Testing:
- Comprehensive test coverage (87%+ for core modules)
- Unit tests for all new modules
- Integration tests for MCP telemetry
- Fixed test isolation issues
Data Management:
- Clear user consent in welcome message
- Batch processing with deduplication
- Automatic workflow flushing
BREAKING CHANGE: TelemetryManager constructor is now private, use getInstance()
🤖 Generated with [Claude Code](https://claude.ai/code)
Co-Authored-By: Claude <noreply@anthropic.com>
Adds zero-configuration anonymous usage statistics to track:
- Number of active users with deterministic user IDs
- Which MCP tools AI agents use most
- What workflows are built (sanitized to protect privacy)
- Common errors and issues
Key features:
- Zero-configuration design with hardcoded write-only credentials
- Privacy-first approach with comprehensive data sanitization
- Opt-out support via config file and environment variables
- Docker-friendly with environment variable support
- Multi-process safe with immediate flush strategy
- Row Level Security (RLS) policies for write-only access
Technical implementation:
- Supabase backend with anon key for INSERT-only operations
- Workflow sanitization removes all sensitive data
- Environment variables checked for opt-out (TELEMETRY_DISABLED, etc.)
- Telemetry enabled by default but respects user preferences
- Cleaned up all debug logging for production readiness
🤖 Generated with [Claude Code](https://claude.ai/code)
Co-Authored-By: Claude <noreply@anthropic.com>
- Implement telemetry manager for tracking tool usage and workflows
- Add workflow sanitizer to remove sensitive data before storage
- Create config manager with opt-in/opt-out mechanism
- Integrate telemetry tracking into MCP server and workflow handlers
- Add CLI commands for telemetry control (enable/disable/status)
- Show first-run notice with clear privacy information
- Add comprehensive unit tests for sanitization and config
- Track tool usage metrics, workflow patterns, and errors
- Ensure complete anonymity with deterministic user IDs
- Never collect URLs, API keys, or sensitive information
