FrontMCP Observability

Router for adding observability to FrontMCP servers. Covers distributed tracing (OpenTelemetry), structured JSON logging, per-request log collection, the this.telemetry developer API, and vendor integrations.

When to Use This Skill

Must Use

• Adding tracing, logging, or monitoring to a FrontMCP server
• Connecting Coralogix, Datadog, Logz.io, Grafana, or any OTLP backend
• Using this.telemetry to create custom spans in tools, plugins, or agents
• Setting up structured logging with winston, pino, or NDJSON stdout
• Testing that spans and log entries are created correctly

Recommended

• Before going to production (see also frontmcp-production-readiness)
• When debugging request latency or error rates
• When building plugins that need trace context propagation

Skip When

• Building a prototype that doesn't need observability yet
• Configuring auth, transport, or throttle (see frontmcp-config)
• Setting up the project from scratch (see frontmcp-setup)

Decision: Use this skill when you need to observe, trace, or log your server. Start with tracing-setup for auto-instrumentation, add structured-logging for production logs, and use telemetry-api for custom spans in your code.

Prerequisites

• A working FrontMCP server (see frontmcp-setup)
• npm install @frontmcp/observability

Step 1: Choose What You Need

Step 2: Enable Observability

The simplest way — one config line:

@FrontMcp({
  observability: true,
})

This enables auto-tracing for all 33 SDK flows. Add structured logging:

@FrontMcp({
  observability: {
    tracing: true,
    logging: { sinks: [{ type: 'stdout' }] },
    requestLogs: true,
  },
})

Step 3: Read the Relevant Reference

Follow the scenario routing table above to find the right reference for your use case.

Scenario Routing Table

Cross-Cutting Patterns

Quick Reference: What Gets Traced

Verification Checklist

Configuration

• @frontmcp/observability installed
• observability field added to @FrontMcp config
• TracerProvider configured (via setupOTel() or external SDK)
• Logging sinks configured for production (stdout or OTLP)

Runtime

• Spans appear in trace backend when calling a tool
• Log entries include trace_id and span_id
• this.telemetry is available in tool execution contexts
• Session tracing ID is consistent across all spans in a request
• Errors are recorded on spans with ERROR status

Testing

• Tests verify span creation with createTestTracer()
• Tests verify log entries via CallbackSink
• No test isolation issues (each test resets exporter)

Reference

• Observability Guide
• Telemetry API Reference
• Related skills: frontmcp-production-readiness, frontmcp-config, frontmcp-testing, frontmcp-development