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OpenTelemetry MCP Server Resources Reference

The TalkOps OpenTelemetry MCP Server exposes 9 read-only resources (otel:// URIs). These endpoints give AI assistants and human operators structured real-time insight into collector configurations, Kubernetes instrumentation states, and cluster security profiles without executing mutating tool calls.

Available Resource URIs​

Resource URIDescriptionExample Target
otel://system/healthOverall system health, Kubernetes API connectivity, and Operator CRD availability.Server diagnostic state
otel://collector/{namespace}/{name}Full parsed collector configuration, pipeline topology, and status.otel://collector/monitoring/otel-gateway
otel://k8s-enrichment/{namespace}/{collector}k8sattributes processor profile and metadata extraction settings.otel://k8s-enrichment/monitoring/otel-gateway
otel://logs-profile/{namespace}/{collector}Filelog receiver paths, multiline parser rules, and safety checks.otel://logs-profile/monitoring/otel-daemonset
otel://spanmetrics/{namespace}/{collector}SpanMetrics connector dimensions, histogram buckets, and wiring.otel://spanmetrics/monitoring/otel-gateway
otel://instrumentation/{namespace}/{name}Instrumentation CRD specifications, propagators, and language images.otel://instrumentation/production/default-inst
otel://target-allocator/{namespace}/{name}Target Allocator scraping configuration and ServiceMonitor matching.otel://target-allocator/monitoring/otel-metrics
otel://lang/{language}Detailed capability matrix for a specific programming language.otel://lang/python
otel://registry/languagesComplete catalog of supported languages and auto-instrumentation tiers.Global language catalog

Resource Usage Examples​

1. Inspecting Collector Topology (otel://collector/monitoring/otel-gateway)​

Calling read_resource("otel://collector/monitoring/otel-gateway") returns a structured view of the active pipelines:

{
  "uri": "otel://collector/monitoring/otel-gateway",
  "mimeType": "application/json",
  "text": {
    "name": "otel-gateway",
    "namespace": "monitoring",
    "mode": "Deployment",
    "replicas": 3,
    "pipelines": {
      "traces": {
        "receivers": ["otlp"],
        "processors": ["memory_limiter", "k8sattributes", "resourcedetection", "batch"],
        "exporters": ["otlp/tempo"]
      },
      "metrics": {
        "receivers": ["otlp", "prometheus"],
        "processors": ["memory_limiter", "batch"],
        "exporters": ["prometheusremotewrite/mimir"]
      }
    },
    "extensions": ["health_check", "zpages"]
  }
}

2. Querying Language Instrumentation Capabilities (otel://lang/python)​

AI assistants use this resource to look up supported frameworks, injection annotations, and SDK packages before attempting to patch workloads:

{
  "uri": "otel://lang/python",
  "mimeType": "application/json",
  "text": {
    "language": "python",
    "auto_instrumentation_supported": true,
    "crd_annotation": "instrumentation.opentelemetry.io/inject-python",
    "supported_frameworks": [
      "FastAPI",
      "Django",
      "Flask",
      "Tornado",
      "Celery"
    ],
    "default_propagators": ["tracecontext", "baggage"],
    "stability": "stable"
  }
}

3. Reviewing System Health (otel://system/health)​

Verify that the MCP server can authenticate with the Kubernetes API server and that the OpenTelemetry Operator CRDs are registered:

{
  "uri": "otel://system/health",
  "mimeType": "application/json",
  "text": {
    "status": "healthy",
    "k8s_connected": true,
    "in_cluster": false,
    "crds": {
      "OpenTelemetryCollector": "v1beta1",
      "Instrumentation": "v1alpha1"
    },
    "transport": "http",
    "version": "1.0.0"
  }
}