api-telemetry

Overview

The framework auto-instruments every tool, resource, prompt, storage, LLM, speech, and graph call — each gets its own span and the standard counters/histograms. HTTP server requests pick up spans from HttpInstrumentation (all Node.js HTTP traffic, skips /healthz) plus httpInstrumentationMiddleware from @hono/otel on the MCP HTTP endpoint when installed (optional Tier 3 peer — bun add @hono/otel). On Bun, HttpInstrumentation silently no-ops and @hono/otel is the only HTTP coverage. Auth checks, session lifecycle, and task lifecycle are tracked as metrics only — auth decorates the active HTTP span with attributes, sessions and tasks emit counters.

requestId, traceId, and tenantId correlate automatically across spans, metrics, and logs. Pino logs get trace_id/span_id injected when a span is active.

For the helper API surface (withSpan, createCounter, createHistogram, buildTraceparent, etc.) — see the api-utils skill, Telemetry section. This skill is the catalog of what is emitted; that one is the reference for how to emit your own.

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Enabling export

OTel is off by default. OTEL_ENABLED=true alone does nothing — you also need an OTLP endpoint. Without an endpoint the SDK is configured but nothing leaves the process.

Env var	Default	Purpose
OTEL_ENABLED	false	Master switch. Must be true to start the SDK.
OTEL_EXPORTER_OTLP_TRACES_ENDPOINT	—	OTLP/HTTP traces endpoint (e.g. http://localhost:4318/v1/traces).
OTEL_EXPORTER_OTLP_METRICS_ENDPOINT	—	OTLP/HTTP metrics endpoint (e.g. http://localhost:4318/v1/metrics).
OTEL_SERVICE_NAME	package.json name	service.name resource attribute.
OTEL_SERVICE_VERSION	package.json version	service.version resource attribute.
OTEL_TRACES_SAMPLER_ARG	1.0	Trace sampling ratio (0–1) for TraceIdRatioBasedSampler.
OTEL_LOG_LEVEL	INFO	OTel diagnostic logger level (NONE/ERROR/WARN/INFO/DEBUG/VERBOSE/ALL).

Metrics push via PeriodicExportingMetricReader every 15 seconds. Traces use BatchSpanProcessor.

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Runtime support

Runtime	Behavior
Node.js / Bun	Full NodeSDK. Auto-instrumentations: HTTP server (Node http hooks; skips /healthz), Pino logs (trace_id/span_id injection). On the HTTP transport, when OTel is enabled and @hono/otel is installed, httpInstrumentationMiddleware is also wired onto the MCP endpoint — fills the gap on Bun, where the Node http auto-instrumentation silently no-ops. Manual spans, custom metrics, and OTLP export work on Bun regardless.
Cloudflare Workers / V8 isolates	NodeSDK is unavailable. SDK init no-ops silently. createCounter/createHistogram/withSpan calls still work via the global OTel API but produce no output unless you wire a Worker-compatible exporter and ctx.waitUntil() for flush.

Cloud platform detection auto-populates resource attributes:

Detected	Attributes set
Cloudflare Workers	cloud.provider=cloudflare, cloud.platform=cloudflare_workers
AWS Lambda	cloud.provider=aws, cloud.platform=aws_lambda, cloud.region from AWS_REGION
GCP Cloud Run / Functions	cloud.provider=gcp, cloud.platform=gcp_cloud_run (or gcp_cloud_functions), cloud.region from GCP_REGION
All	deployment.environment.name from config.environment

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Spans

Every handler call gets a span. Nested operations (storage, graph, LLM) become child spans on the same trace. All spans carry code.function.name and code.namespace for code-attribution. Errors are recorded via span.recordException() and SpanStatusCode.ERROR; McpError codes surface as the *.error_code attribute.

Span name	Source	Key attributes
tool_execution:<tool>	every tool call	mcp.tool.input_bytes, mcp.tool.output_bytes, mcp.tool.duration_ms, mcp.tool.success, mcp.tool.error_code, mcp.tool.partial_success, mcp.tool.batch.{succeeded,failed}_count
resource_read:<resource>	every resource handler	mcp.resource.uri, mcp.resource.mime_type, mcp.resource.size_bytes, mcp.resource.duration_ms, mcp.resource.success, mcp.resource.error_code
prompt_generation:<prompt>	every prompt handler	mcp.prompt.input_bytes, mcp.prompt.output_bytes, mcp.prompt.message_count, mcp.prompt.duration_ms, mcp.prompt.success, mcp.prompt.error_code
storage:<op>	StorageService (every call)	mcp.storage.operation, mcp.storage.duration_ms, mcp.storage.success, mcp.storage.key_count (batch ops)
graph:<op>	GraphService (every call)	mcp.graph.operation, mcp.graph.duration_ms, mcp.graph.success
gen_ai.chat_completion	OpenRouter LLM provider	gen_ai.system=openrouter, gen_ai.request.model, gen_ai.request.{max_tokens,temperature,top_p,streaming}, gen_ai.response.model, gen_ai.usage.{input,output,total}_tokens
speech:tts	ElevenLabs provider	mcp.speech.provider, mcp.speech.operation, mcp.speech.input_bytes, mcp.speech.output_bytes, mcp.speech.duration_ms, mcp.speech.success
speech:stt	Whisper provider	same as speech:tts

Trace context propagates across boundaries via W3C traceparent headers. See api-utils → telemetry/trace for withSpan, buildTraceparent, extractTraceparent, createContextWithParentTrace, injectCurrentContextInto, runInContext signatures.

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Metrics

All custom metrics are namespaced mcp.* (or process.* / http.client.* where standard semconv applies). Lazy-initialized on first emission; tool, resource, prompt, http.client.request.duration, heartbeat, session, auth, rate-limit, and error metrics are eagerly created at startup so series exist from the first export cycle. LLM, speech, graph, and storage instruments are lazy-initialized on first use.

Tools, resources, prompts

Metric	Type	Unit	Attributes
mcp.tool.calls	counter	{calls}	mcp.tool.name, mcp.tool.success
mcp.tool.duration	histogram	ms	mcp.tool.name, mcp.tool.success
mcp.tool.errors	counter	{errors}	mcp.tool.name, mcp.tool.error_category (upstream/server/client)
mcp.tool.input_bytes	histogram	bytes	mcp.tool.name
mcp.tool.output_bytes	histogram	bytes	mcp.tool.name (success only)
mcp.tool.param.usage	counter	{uses}	mcp.tool.name, mcp.tool.param (top-level keys supplied by caller)
mcp.resource.reads	counter	{reads}	mcp.resource.name, mcp.resource.success
mcp.resource.duration	histogram	ms	mcp.resource.name, mcp.resource.success
mcp.resource.errors	counter	{errors}	mcp.resource.name
mcp.resource.output_bytes	histogram	bytes	mcp.resource.name (success only)
mcp.prompt.generations	counter	{generations}	mcp.prompt.name, mcp.prompt.success
mcp.prompt.duration	histogram	ms	mcp.prompt.name, mcp.prompt.success
mcp.prompt.errors	counter	{errors}	mcp.prompt.name, mcp.prompt.error_category
mcp.prompt.input_bytes	histogram	bytes	mcp.prompt.name
mcp.prompt.output_bytes	histogram	bytes	mcp.prompt.name (success only)
mcp.prompt.message_count	histogram	{messages}	mcp.prompt.name
mcp.requests.active	up/down counter	{requests}	— (in-flight handler executions, all three types)

Storage, LLM, speech, graph

Metric	Type	Unit	Attributes
mcp.storage.operations	counter	{ops}	mcp.storage.operation, mcp.storage.success
mcp.storage.duration	histogram	ms	mcp.storage.operation, mcp.storage.success
mcp.storage.errors	counter	{errors}	mcp.storage.operation
mcp.llm.requests	counter	{requests}	gen_ai.system, gen_ai.request.model
mcp.llm.duration	histogram	ms	gen_ai.system, gen_ai.request.model
mcp.llm.errors	counter	{errors}	gen_ai.system, gen_ai.request.model
mcp.llm.tokens	counter	{tokens}	gen_ai.request.model, gen_ai.token.type (input/output)
mcp.speech.operations	counter	{ops}	mcp.speech.operation (tts/stt), mcp.speech.provider, mcp.speech.success
mcp.speech.duration	histogram	ms	mcp.speech.operation, mcp.speech.provider
mcp.speech.errors	counter	{errors}	mcp.speech.operation, mcp.speech.provider
mcp.graph.operations	counter	{ops}	mcp.graph.operation, mcp.graph.success
mcp.graph.duration	histogram	ms	mcp.graph.operation, mcp.graph.success
mcp.graph.errors	counter	{errors}	mcp.graph.operation

Transport, auth, sessions, tasks

Metric	Type	Unit	Attributes
mcp.auth.attempts	counter	{attempts}	mcp.auth.outcome (success/failure/missing), mcp.auth.failure_reason
mcp.auth.duration	histogram	ms	mcp.auth.outcome, mcp.auth.failure_reason
mcp.sessions.events	counter	{events}	mcp.session.event (created/terminated/rejected/stale_cleanup)
mcp.session.duration	histogram	s	—
mcp.sessions.active	observable gauge	{sessions}	—
mcp.heartbeat.failures	counter	{failures}	mcp.connection.transport (stdio/http)
mcp.http.close_failures	counter	{failures}	surface (transport/server), trigger (success/error/sse-abort) — per-request close threw or timed out
mcp.http.per_request.created	counter	{instances}	kind (server/transport) — per-request McpServer and McpSessionTransport instances created
mcp.http.per_request.finalized	counter	{instances}	kind (server/transport) — per-request instances reclaimed by GC; persistent gap vs created indicates a leak
mcp.tasks.created	counter	{tasks}	mcp.task.store_type (in-memory/storage)
mcp.tasks.status_changes	counter	{transitions}	mcp.task.status, mcp.task.store_type
mcp.tasks.active	observable gauge	{tasks}	— (in-memory store only)

Errors, rate limits, HTTP client

Metric	Type	Unit	Attributes
mcp.errors.classified	counter	{errors}	mcp.error.classified_code (JSON-RPC code), operation
mcp.ratelimit.rejections	counter	{rejections}	mcp.rate_limit.key
http.client.request.duration	histogram	s	http.request.method, server.address, http.response.status_code (when > 0; absent on network errors before a response is received)

Process

Auto-registered when process.memoryUsage / process.uptime / perf_hooks are available (Node/Bun, not Workers). The three memory gauges share a single process.memoryUsage() snapshot per collection cycle, refreshed at most every 100 ms.

Metric	Type	Unit	Notes
process.memory.rss	observable gauge	bytes	Resident set size
process.memory.heap_used	observable gauge	bytes	V8 heap used
process.memory.heap_total	observable gauge	bytes	V8 total heap
process.uptime	observable gauge	s	Process uptime
process.event_loop.delay	observable gauge	ms	p99 delay (monitorEventLoopDelay resolution=20)
process.event_loop.utilization	observable gauge	1	0 = idle, 1 = saturated

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Logs

Pino logs are auto-instrumented by @opentelemetry/instrumentation-pino. When a span is active, trace_id and span_id are injected into the record. Combined with the framework logger's automatic requestId/tenantId correlation, every log line is searchable by trace.

For domain logging inside handlers, use ctx.log (debug/info/notice/warning/error) — auto-includes requestId, traceId, tenantId, spanId. The completion log emitted at the end of every handler carries a metrics payload, with fields tuned to each surface:

Handler	Log message	metrics fields
Tool	Tool execution finished.	durationMs, isSuccess, errorCode, inputBytes, outputBytes, plus partialSuccess / batchSucceeded / batchFailed when the result is a partial-success batch
Resource	Resource read finished.	durationMs, isSuccess, errorCode, outputBytes, uri, mimeType
Prompt	Prompt generation finished. (or failed.)	durationMs, isSuccess, errorCode, inputBytes, outputBytes, messageCount

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Custom instrumentation

Need a span or metric for your own service? Use the helpers from @cyanheads/mcp-ts-core/utils (full signatures in api-utils → Telemetry):

import { withSpan, createCounter, createHistogram } from '@cyanheads/mcp-ts-core/utils';

const myOps = createCounter('myservice.operations', 'My service ops', '{ops}');
const myDuration = createHistogram('myservice.duration', 'My service duration', 'ms');

export async function doWork() {
  return withSpan('myservice.do_work', async (span) => {
    const t0 = performance.now();
    try {
      const result = await reallyDoWork();
      span.setAttribute('myservice.items', result.length);
      return result;
    } finally {
      myDuration.record(performance.now() - t0);
      myOps.add(1);
    }
  }, { 'myservice.region': 'us-west' });
}

Span context propagates automatically — withSpan calls inside a tool_execution:* span appear as children. runInContext(ctx, fn) carries the active OTel context across async boundaries (setTimeout, queueMicrotask).

For attribute keys, prefer the ATTR_* constants exported from @cyanheads/mcp-ts-core/utils (telemetry/attributes) over hand-typed strings — keeps you in step with framework conventions and avoids typos. Standard OTel semantic conventions (HTTP, cloud, service, network, etc.) are NOT re-exported — import those directly from @opentelemetry/semantic-conventions.

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Visualization

An example Grafana dashboard JSON and vendor-agnostic query recipes (Prometheus, Datadog, New Relic, Honeycomb) live at docs/telemetry/ in the framework source — not bundled in the npm package, so consult the GitHub repo.

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Cardinality discipline

Series are cheap to emit but expensive to store and query. The framework deliberately keeps high-cardinality identifiers off metric attributes and on spans only. Follow the same rule when adding your own metrics.

On metrics	On spans / logs only
mcp.resource.name (URI template)	mcp.resource.uri (full URI with IDs)
gen_ai.request.model (bounded enum)	mcp.tenant.id, mcp.client.id, mcp.auth.subject
Bounded enum / template strings	Per-request unique IDs, free-form user input, opaque tokens

When in doubt: if the attribute can take more than ~100 distinct values across a fleet's runtime, it belongs on the span, not the metric.