◎ Discry Score
datadog.com
devtools · API
B
0 / 100
DISCOVERY0
COMPREHENSION0
Category leader: 96 (A)
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DEVTOOLS · RANK #7 OF 39

Datadog is Good to agents.

Discry independently scored how well an AI agent can discover and understand the Datadog API from what’s public — not whether it’s usable. Below: every signal we checked, what’s costing the score, and what to change.

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SCORED UNDER RUBRIC 1.2 · A full re-launch under Discry Score 2.5 — a new behavioral instrument, not comparable to these scores — is in progress.

Discovery

45% of score · 86/100
OpenAPI specA machine-readable OpenAPI/Swagger spec agents can parse.Pass
llms.txtAn llms.txt index that points agents to the docs that matter.Pass
llms.txt qualityThe llms.txt is focused, current, and well under the size budget.Partial
llms-full.txtA full-text bundle agents can load in one request.Fail
AGENTS.mdAn AGENTS.md that tells coding agents how to build on the API.Pass
.well-known/mcp.jsonA discoverable MCP manifest at a well-known path.Fail
MCP registryThe API is listed in a public MCP registry.Pass
robots.txt AI directivesrobots.txt allows (or explicitly guides) AI crawlers.Pass
SitemapA sitemap so agents can enumerate the docs surface.Pass

Comprehension

55% of score · 91/100
Task-oriented descriptionsEndpoints described by what they accomplish, not just their shape.Pass
Realistic examplesRunnable, real-world request/response examples.Pass
Multi-step workflowsDocs that chain calls into complete jobs an agent can follow.Pass
Error-recovery guidanceDocumented failure modes and how to recover from them.Partial
Answer-first formatThe answer leads; preamble does not bury it.Pass
Capability boundariesClear limits — what the API can and cannot do.Pass
Naming consistencyConsistent, predictable naming across endpoints.Pass
Heading hierarchyClean heading structure agents can navigate.Pass
Markdown docsDocs available as clean markdown, not JS-rendered HTML only.Pass
Token efficiencyDocs are concise enough to fit an agent context window.Partial

What we found

  • Datadog has a massive llms.txt (1.8MB) that serves as a comprehensive documentation index with .md links for every page — extremely thorough but too large for single-shot agent consumption.
  • AGENTS.md files exist across 6+ repos (datadog-agent, system-tests, dd-trace-js, dd-trace-dotnet, java-profiler, browser-sdk, datadog-api-claude-plugin) showing strong agent-tooling investment.
  • An official Datadog MCP Server exists with OAuth-based authentication, plus a dedicated datadog-api-claude-plugin repo demonstrating first-party AI agent integration.
  • No llms-full.txt exists despite having the llms.txt — the llms.txt itself IS the full dump, which defeats the tiered progressive disclosure pattern.
  • OpenAPI specs drive auto-generated client libraries in Go, Java, Python, TypeScript — agents can use these SDKs or the spec directly for API interaction.

What to change

Prioritized by impact on discoverability. You (or your docs platform) deploy these — Discry never touches your API.

  1. 01Split the 1.8MB llms.txt into a concise index (<50KB) covering API-focused documentation, with the full content moved to llms-full.txt for bulk indexing.
  2. 02Add .well-known/mcp.json pointing to the official Datadog MCP server to enable machine discovery.
  3. 03Create a dedicated API-only llms.txt (separate from the product docs index) that covers just the REST API endpoints, authentication, and SDK usage patterns.
  4. 04Add explicit error recovery guidance to the API reference — beyond error codes, document what agents should do when encountering common errors.
  5. 05Publish the OpenAPI spec at a well-known URL (e.g., docs.datadoghq.com/openapi.json) rather than only in GitHub repos.

Execution coverage · INFORMATIONAL, UNSCORED

Whether an agent can actually complete a call and recover from errors is the deeper Audit layer — documented here, but not part of the Discry Score.

API key + Application keyOAuth2✓ Error format documented✓ Rate limits documented✓ Pagination documented✕ Idempotency documented

Well-documented execution with API key + application key auth, OAuth2 for integrations, structured JSON error responses, rate limiting per endpoint, and cursor-based pagination. Auto-generated client libraries in multiple languages from OpenAPI specs.

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