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Jaeger

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Provides read-only access to Jaeger distributed tracing data through the Model Context Protocol. Enables Claude and other MCP-capable agents to search traces, i

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About

Provides read-only access to Jaeger distributed tracing data through the Model Context Protocol. Enables Claude and other MCP-capable agents to search traces, inspect spans, and analyze service dependencies directly within conversations.

README

PyPI version Python versions License: MIT Tests

MCP server for Jaeger distributed tracing. Give Claude (or any MCP-capable agent) read access to your trace data — search traces, inspect spans, compare traces, compute span statistics, map service dependencies, predict performance issues, and forecast capacity needs — without leaving the conversation.

Why another Jaeger MCP?

The existing Jaeger integrations require a running UI or custom scripts. This server:

  • Speaks the standard Model Context Protocol over stdio — works with Claude Desktop, Claude Code, Cursor, and any MCP client.
  • Is read-only: all 12 tools carry readOnlyHint: true — zero risk of modifying trace data.
  • Returns dual-channel output: structured JSON (structuredContent) for programmatic use + Markdown (content) for human-readable display.
  • Has actionable error messages that name the exact env var to fix and suggest a next step.
  • Supports Bearer token, HTTP Basic auth, or no auth (common for internal deployments).
  • Includes OpenAPI specification documenting the underlying Jaeger Query API (openapi.yaml).

Tools

Tool Endpoint Description
jaeger_list_services GET /api/services List all instrumented services
jaeger_list_operations GET /api/services/{service}/operations List operation names for a service
jaeger_search_traces GET /api/traces Search traces with rich filters
jaeger_get_trace GET /api/traces/{traceID} Full trace detail with span tree
jaeger_get_dependencies GET /api/dependencies Service-to-service call graph
jaeger_compare_traces GET /api/traces/{traceID} ×2 Structural diff between two traces
jaeger_span_statistics GET /api/traces Per-operation latency and error stats
jaeger_critical_path GET /api/traces/{traceID} Longest-duration span chain and bottleneck ranking
jaeger_compare_windows GET /api/traces ×2 Aggregate trace behavior diff between two time periods
jaeger_detect_anomalies GET /api/traces ×2 Statistical latency/error-rate spike detection per operation
jaeger_predict_degradation GET /api/traces Predict performance degradation 2-24 hours in advance
jaeger_forecast_capacity GET /api/traces Forecast throughput demands and resource requirements

Installation

pip install jaeger-mcp

Or run directly without installing:

uvx jaeger-mcp

Configuration

All configuration is via environment variables:

Variable Required Default Description
JAEGER_URL Yes Jaeger query service URL, e.g. https://jaeger.example.com
JAEGER_TOKEN No Bearer token (takes precedence over Basic auth)
JAEGER_USERNAME No HTTP Basic auth username
JAEGER_PASSWORD No HTTP Basic auth password
JAEGER_SSL_VERIFY No true Set false for self-signed certificates
JAEGER_TIMEOUT No 30 HTTP request timeout in seconds
JAEGER_RETRY_ATTEMPTS No 3 Retry count for transient failures (0 to disable)
JAEGER_CACHE_TTL No 120 TTL in seconds for discovery endpoint cache (0 to disable)

Copy .env.example to .env and fill in your values.

Claude Desktop / Claude Code setup

Add to your MCP config (claude_desktop_config.json or .claude/mcp.json):

{
  "mcpServers": {
    "jaeger": {
      "command": "jaeger-mcp",
      "env": {
        "JAEGER_URL": "https://jaeger.example.com",
        "JAEGER_TOKEN": "your-token-here"
      }
    }
  }
}

Or with uvx (no install required):

{
  "mcpServers": {
    "jaeger": {
      "command": "uvx",
      "args": ["jaeger-mcp"],
      "env": {
        "JAEGER_URL": "https://jaeger.example.com"
      }
    }
  }
}

Docker

docker run --rm -e JAEGER_URL=https://jaeger.example.com jaeger-mcp

Example queries

Once configured, ask Claude:

  • "What services does Jaeger know about?"
  • "Find traces with HTTP 500 errors in order-service from the last hour"
  • "Show me the slowest traces (over 2 seconds) for GET /checkout"
  • "What caused the error in trace abcdef1234567890?"
  • "Map the service dependency graph for the last 7 days"
  • "Which services call postgres most frequently?"
  • "Compare trace abc123 against trace def456 — what spans changed?"
  • "What are the p95 latencies per operation in order-service?"

Tool usage guide

jaeger_list_services

Returns all service names Jaeger has seen. Start here when you don't know which services are instrumented. Output is capped at 500 services with a truncation hint.

jaeger_list_operations

Returns all operation names for a given service (e.g. HTTP route names, gRPC method names). Use to discover valid operation names before filtering jaeger_search_traces.

jaeger_search_traces

The main search tool. Filters:

  • service (required) — service name from jaeger_list_services
  • operation — narrow to a specific endpoint
  • tags — JSON string of tag filters, e.g. {"http.status_code":"500"} or {"error":"true"}
  • start / end — time range in microseconds UTC
  • min_duration / max_duration — duration strings like "100ms", "1.5s", "2m"
  • limit — default 20, max 1500

Returns trace summaries with trace_id, duration_us, span_count, service_count, root_operation, errors_count.

jaeger_get_trace

Full trace detail. Accepts a trace_id (hex string, 16-32 chars) and returns:

  • All spans with tags, service names, parent/child relationships
  • Per-service statistics (span count, total duration, error count)
  • Execution tree (each node lists its child span IDs)

Error spans are identified by tags["error"] = "true".

jaeger_get_dependencies

Service topology graph. Returns directed edges (parent → child) with call_count. Use lookback_hours (default 24, max 720) to control the window.

jaeger_compare_traces

Structural diff between two traces. Accepts two trace_id hex strings and matches spans by (operationName, serviceName, parentOperation) — not span ID. Reports:

  • Added spans — present in trace B but not trace A
  • Removed spans — present in trace A but not trace B
  • Changed spans — matched but differ in duration or tags (shows deltas)
  • Unchanged count — number of identical spans

Use to compare a slow trace against a fast one, or to see what changed between deployments.

jaeger_span_statistics

Per-operation latency percentiles and error rates. Fetches up to limit traces (default 20, max 100) for a service and aggregates all spans by operation name. Reports per operation:

  • count — total spans observed
  • p50_duration_us, p95_duration_us, p99_duration_us — latency percentiles
  • error_count, error_rate — errors (identified by tags["error"] = "true")

Use to find the slowest or most error-prone operations in a service.

jaeger_critical_path

Identifies the longest-duration span chain from root to leaf in a trace (the critical path) and ranks spans by self-time to find performance bottlenecks.

Reports:

  • Critical path spans with operation, service, duration, and percentage-of-total
  • Bottleneck spans ranked by exclusive duration (self-time)

Use to answer "Why is this trace so slow?" and "Which operations consume the most CPU/self-time?"

jaeger_compare_windows

Compares aggregate trace behavior between two time periods for a service to detect performance regressions or improvements across deployments.

Reports:

  • Per-operation diff summary showing added, removed, faster, slower operations
  • Deviation scoring with numeric scores per operation and overall
  • Latency percentile changes (p50, p95) and error rate deltas

Use to answer "Did our latest deployment affect performance?" and "Which operations got slower after the database upgrade?"

jaeger_detect_anomalies

Scans for statistically significant latency spikes or error-rate increases in a service's recent traces compared to historical baselines.

Reports:

  • Flagged operations with anomaly type (latency or error_rate)
  • Severity classification (low to critical) with z-scores
  • Current vs baseline values for affected metrics

Use to proactively identify performance degradations and reliability issues before they impact users.

Library facade (in-process use)

jaeger-mcp can also be used as a Python library without an MCP server:

from jaeger_mcp import JaegerClient

client = JaegerClient.from_env()  # reads JAEGER_URL from env
trace = client.get_trace("abcdef1234567890abcdef1234567890")

for span in trace.spans:
    if span.error:
        print(f"{span.service_name}: {span.operation} at {span.start_utc}")
        print(f"  tags: {span.tags}")

Available methods: get_trace(), search_traces(), list_services(), get_dependencies(), compare_traces(), span_statistics(), critical_path(), compare_windows(), detect_anomalies().

Domain objects: Span, Trace, TraceSummary, ServiceDep, TraceComparison, SpanIdentity, SpanChange, SpanStatisticsResult, OperationStatResult, CriticalPathOutput, CriticalPathSpan, BottleneckSpan, WindowComparisonOutput, OperationDiff, AnomalyDetectionOutput, OperationAnomaly — all with typed fields.

API Documentation

This project includes comprehensive OpenAPI specifications in the docs/ directory:

  1. Jaeger Query Service API (openapi.yaml) - Documents the actual Jaeger API endpoints
  2. MCP Tools API (docs/mcp-tools-openapi.yaml) - Documents the MCP tools as conceptual HTTP endpoints

These specifications are useful for:

  • Understanding the underlying API calls made by each tool
  • Developing alternative integrations
  • Debugging API interactions
  • Generating client libraries or documentation

See docs/README.md for more details on both specifications.

Performance characteristics

  • All tools use a single persistent requests.Session with connection pooling.
  • The session has trust_env = False to bypass environment proxies (Jaeger is typically an internal service).
  • Requests time out after 30 seconds (configurable via JAEGER_TIMEOUT).
  • Transient HTTP errors (429/5xx) are retried with exponential backoff (configurable via JAEGER_RETRY_ATTEMPTS).
  • list_services and list_operations responses are cached for 120 seconds (configurable via JAEGER_CACHE_TTL).
  • jaeger_search_traces passes limit directly to Jaeger — avoid requesting more traces than needed.
  • jaeger_get_trace fetches the full trace in one call — large traces (thousands of spans) may be slow.
  • jaeger_get_dependencies aggregates over the full lookback window; large windows may be slow on busy clusters.

Development

git clone https://github.com/mshegolev/jaeger-mcp
cd jaeger-mcp
pip install -e '.[dev]'
pytest tests/ -v
ruff check src tests
ruff format src tests

License

MIT — see LICENSE.

from github.com/mshegolev/jaeger-mcp

Install Jaeger in Claude Desktop, Claude Code & Cursor

Recommended · one command, every IDE
unyly install jaeger-mcp

Installs into Claude Desktop, Claude Code, Cursor & VS Code — handles npx, uvx and build-from-source repos for you.

First time? Get the CLI: curl -fsSL https://unyly.org/install | sh

Or configure manually

Run in your terminal:

claude mcp add jaeger-mcp -- uvx jaeger-mcp

Step-by-step: how to install Jaeger

FAQ

Is Jaeger MCP free?

Yes, Jaeger MCP is free — one-click install via Unyly at no cost.

Does Jaeger need an API key?

No, Jaeger runs without API keys or environment variables.

Is Jaeger hosted or self-hosted?

A hosted option is available: Unyly runs the server in the cloud, no local setup required.

How do I install Jaeger in Claude Desktop, Claude Code or Cursor?

Open Jaeger on unyly.org, pick your client tab (Claude Desktop, Claude Code, Cursor) and press Install — the config is generated automatically, no JSON editing.

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