OpenTelemetry traces for AI coding agents via MCP

otel-mcp

MCP server that gives AI agents access to your application's OpenTelemetry traces.

Agent calls: list_traces { has_errors: true }

Recent Traces (2 of 847)

TRACE ID          SERVICE        DURATION     SPANS  ERRORS  ROOT
a]b7f2e9d4c8      checkout-api      2.34s        12       1  POST /checkout
f3e1a8b2c6d9      checkout-api      1.87s         8       1  POST /checkout

Agent calls: get_trace { trace_id: "a]b7f2e9d4c8" }

Trace ab7f2e9d4c8

Services: checkout-api, inventory-service, postgres
Duration: 2.34s
Spans: 12, 1 error

SPAN TREE
----------------------------------------------------------------
[2.34s] POST /checkout
  [1.92s] OrderService.create
    [1.87s] InventoryService.reserve  ← HTTP 500
      [45ms] POST inventory-service/reserve
    [23ms] pg.query SELECT * FROM products...
  [412ms] PaymentService.charge
    [401ms] stripe.charges.create

The agent can query traces, find errors, identify slow operations - without you copying logs into chat.

Why This Exists

AI agents can read code, but they can't see how it executes. When debugging locally, you end up checking traces yourself and explaining what you found. That's the bottleneck.

otel-mcp removes that step by letting agents query execution data directly.

Read more:

• How to Give AI Agents Access to Runtime Traces — practical guide
• Why AI Development Tools Must Be Execution-Aware — the design principle

Architecture

flowchart LR
    subgraph app["Your Application"]
        OTel["OpenTelemetry SD