Observability

FrontMCP has native observability built in. Enable it with a single config line and get automatic distributed tracing, structured JSON logging, and per-request log collection across your entire server — every tool call, resource read, prompt invocation, auth flow, and transport session.

Capability	What It Does
OpenTelemetry tracing	Automatic spans for all 33 SDK flows with parent-child hierarchy
Structured logging	JSON log entries with trace correlation, sent to any backend
Request log collection	Per-request aggregated view of all events during a request lifecycle
`this.telemetry` API	Developer-facing API for custom spans, events, and attributes in tools
Vendor integrations	OTLP, Coralogix, Datadog, Logz.io, Grafana, winston, pino

How It Works

@FrontMcp({
  info: { name: 'my-server', version: '1.0.0' },
  apps: [MyApp],
  observability: true, // That's it
})
class Server {}

To call this.telemetry from TypeScript, also import the package where you use it (or once in any compiled file). Its type is a declare module '@frontmcp/sdk' augmentation that TypeScript sees only when the package is part of the compilation:

import type {} from '@frontmcp/observability'; // declares this.telemetry on tool, resource, prompt and agent contexts

When enabled:

Zero config — 103+ hooks register automatically on every flow
Single trace ID — all spans in a request share the same W3C trace ID
Privacy-safe session ID — a truncated SHA-256 hash, never the real session ID
Zero overhead when unused — without a TracerProvider, all OTel calls are no-ops
MCP-interoperable — uses rpc.system = "mcp" and mcp.* attributes for cross-framework tracing

What Gets Instrumented

Every flow in the SDK is automatically traced:

Category	Flows
HTTP & Transport	HTTP requests, SSE sessions, Streamable HTTP, Stateless HTTP
Tools	call-tool (every stage), list-tools
Resources	read-resource, list-resources, list-templates, subscribe, unsubscribe
Prompts	get-prompt, list-prompts
Agents	call-agent (with nested tool call tracking)
Auth	auth:verify, session:verify
OAuth	token, authorize, callback, provider-callback, register
Skills	search, load, llm-txt, llm-full-txt, API
Elicitation	request, result
Other	completion, well-known endpoints, logging/set-level

Logging vs Tracing

They’re complementary, not competing:

	Logging	Tracing
What	Event stream — discrete log lines	Request lifecycle — spans with timing
Purpose	Debug, audit, error forensics	Performance analysis, dependency mapping
Export to	Stdout, winston, pino, OTLP	OTLP, Jaeger, Zipkin
Connection	`trace_id` on every log line	Native parent-child span tree

A log line says “what happened.” A span says “how long it took.” When both share the same trace_id, platforms like Coralogix or Datadog show logs inline within a trace waterfall.

Learn More

Setup Guide

Step-by-step: install, configure backends, use this.telemetry

Telemetry API Reference

Full API for TelemetryAccessor, TelemetrySpan, and testing utilities

OpenTelemetry

OpenTelemetry documentation

Plugin System

How plugins hook into the request lifecycle

Get Started

Concepts

Build

Deploy

Plugins

Adapters

Extensibility

Testing

React SDK

Guides

How It Works

What Gets Instrumented

Logging vs Tracing

Learn More

Setup Guide

Telemetry API Reference

OpenTelemetry

Plugin System

​How It Works

​What Gets Instrumented

​Logging vs Tracing

​Learn More

Setup Guide

Telemetry API Reference

OpenTelemetry

Plugin System

How It Works

What Gets Instrumented

Logging vs Tracing

Learn More