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Trace LLM calls and AI agents with OpenTelemetry. Inspect token usage, cost, latency, and errors with TypeScript, Python, or an existing OTLP integration.

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Checks

• HTTPS5/5

  Served over HTTPS.

• llms.txt12/12

  Found /llms.txt (13027 chars).

• Agent manifest0/15

  No /.well-known/ai-agent.json.

  Fix: Publish https://telemetry.dev/.well-known/ai-agent.json with name, description, capabilities, contact, auth, and pricing.

• A2A agent card0/12

  No /.well-known/agent-card.json.

  Fix: Publish https://telemetry.dev/.well-known/agent-card.json with name, url, and skills or capabilities.

• MCP endpoint0/12

  No MCP document or endpoint found.

  Fix: Publish /.well-known/mcp.json (or an MCP resource metadata document) pointing at your MCP server.

• OpenAPI spec12/12

  Valid OpenAPI/Swagger document.

• Named capabilities8/8

  4 named capabilities (4 with descriptions).

• I/O schemas8/8

  Found 41 schema signals.

• Machine-readable pricing4/8

  Pricing mentioned in llms.txt, but not as structured data.

  Fix: Add a pricing object to ai-agent.json, or publish /pricing.json with currency and product amounts.

• Auth, contact, rate limits5/8

  Declared: contact, auth.

  Fix: Declare contact, auth, and rateLimit in ai-agent.json (and OpenAPI info.contact / securitySchemes).

5 checks short of a perfect 100. The fix lines above are the report — what to publish, at which path.

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