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Observability Guide

Complete guide for distributed tracing and observability in the DevOps AI Toolkit MCP server.

Overview​

What it does: Provides OpenTelemetry-based distributed tracing for debugging complex workflows, measuring AI provider performance, and understanding Kubernetes operation latency.

Use when: You need to understand where time is spent in multi-step workflows, debug performance issues, or monitor AI/Kubernetes operations in production.

📖 Full Guide: This document covers tracing setup, configuration, backend integration, and trace interpretation specific to the DevOps AI Toolkit.

What is Distributed Tracing?​

Learn about distributed tracing concepts and OpenTelemetry:

This guide focuses on DevOps AI Toolkit-specific tracing implementation, configuration, and usage patterns.

Prerequisites​

  • DevOps AI Toolkit MCP server configured (see MCP Setup)
  • Basic understanding of distributed tracing concepts (optional but helpful)
  • Backend for viewing traces (Jaeger, Grafana Tempo, vendor service) or use console output

Quick Start​

Environment Variables​

Add tracing environment variables to your MCP client configuration (see MCP Setup for how to configure environment variables).

VariableRequiredDefaultDescription
OTEL_TRACING_ENABLEDYesfalseEnable/disable tracing
OTEL_SERVICE_NAMENodot-ai-mcpService name in traces
OTEL_EXPORTER_TYPENoconsoleExporter type: console, otlp, jaeger, zipkin
OTEL_EXPORTER_OTLP_ENDPOINTRequired for OTLP-OTLP endpoint URL (e.g., http://localhost:4318/v1/traces)
OTEL_SAMPLING_PROBABILITYNo1.0Sampling rate: 0.0 to 1.0 (1.0 = 100%, 0.1 = 10%)
OTEL_DEBUGNofalseEnable debug logging for tracing

Verify Tracing Status​

After configuring and restarting your MCP client, verify tracing status:

User: Show me the system status

Agent: The system is healthy and all components are operational:

...

Tracing: Enabled
- Exporter: console
- Service Name: dot-ai-mcp
- Status: initialized

The agent will report tracing configuration as part of the system status.

What Gets Traced​

The DevOps AI Toolkit automatically traces all operations without requiring code changes:

MCP Tool Execution​

  • All MCP tools (recommendations, remediation, capability management, etc.)
  • Tool parameters and execution duration
  • Success/failure status
  • Session IDs for workflow correlation

AI Provider Operations​

  • Chat completions: Claude, OpenAI, Google, xAI, and custom endpoints
  • Tool loop iterations: Multi-step AI workflows with per-iteration visibility
  • Embeddings generation: Vector embeddings for semantic search
  • Token usage: Input tokens, output tokens, cache metrics
  • Model information: Provider names and specific model versions

Kubernetes Operations​

  • API client calls: All Kubernetes API operations through the client library
  • kubectl commands: CLI command execution with operation details
  • Resource information: Resource types, namespaces, and operation latency

Vector Database Operations​

  • Search queries: Semantic and keyword searches with result counts
  • Document operations: Upserts, deletions, and retrievals
  • Collection management: Collection operations and health checks
  • Performance metrics: Query latency and result quality scores

Backend Integration​

Jaeger​

Jaeger is an open-source distributed tracing platform. Run Jaeger locally with Docker:

docker run -d --name jaeger \
  -p 16686:16686 \
  -p 4318:4318 \
  jaegertracing/all-in-one:latest

Configure the MCP server to send traces to Jaeger:

OTEL_TRACING_ENABLED=true
OTEL_EXPORTER_OTLP_ENDPOINT=http://localhost:4318/v1/traces

Access the Jaeger UI at http://localhost:16686 to view traces.

Other Backends​

Any tracing backend that supports OpenTelemetry OTLP protocol should work with the same configuration pattern:

OTEL_TRACING_ENABLED=true
OTEL_EXPORTER_OTLP_ENDPOINT=<your-backend-otlp-endpoint>

Refer to your backend's documentation for the specific OTLP endpoint URL.

Viewing Traces​

Jaeger UI​

Open Jaeger UI at http://localhost:16686 (if using local Jaeger setup).

Finding Traces:

  1. Select dot-ai-mcp from the Service dropdown
  2. Click "Find Traces" button
  3. View list of recent traces with duration and span count

Trace Details:

  • Click on a trace to see the complete request flow
  • Spans are displayed in a waterfall timeline showing parent-child relationships
  • Each span shows operation name, duration, and timing relative to the trace start
  • Click on individual spans to see detailed attributes

Understanding Trace Information​

Tool Execution Span:

  • Operation name: execute_tool <tool-name>
  • Shows total time for tool execution
  • Contains session ID and tool parameters

AI Provider Spans:

  • Operation names: chat <model>, tool_loop <model>, embeddings <model>
  • Token usage: gen_ai.usage.input_tokens, gen_ai.usage.output_tokens
  • Cache metrics: gen_ai.usage.cache_read_tokens, gen_ai.usage.cache_creation_tokens
  • Model details: gen_ai.request.model, gen_ai.provider.name

Kubernetes Operation Spans:

  • Operation names: API method names or kubectl <command>
  • Attributes: k8s.api, k8s.method, k8s.operation, k8s.resource
  • Shows latency for Kubernetes API calls

Vector Database Spans:

  • Operation names: search, upsert, delete, list, etc.
  • Attributes: db.operation.name, db.collection.name
  • Result metrics: db.query.result_count, db.vector.top_score

Trace Hierarchy​

All spans from a single tool invocation share the same trace ID and follow this hierarchy:

execute_tool <tool-name>                    (root span)
├── chat <model>                           (AI operation)
│   └── POST https://api.anthropic.com     (HTTP call)
├── search                                 (vector DB query)
│   └── POST http://localhost:6333         (HTTP call)
└── k8s.listNamespacedDeployment          (Kubernetes API)
    └── GET https://kubernetes/apis/apps   (HTTP call)

This hierarchy helps identify which operations are taking the most time and where bottlenecks occur.