◎ Discry Score
posthog.com
analytics · API
B
0 / 100
DISCOVERY0
COMPREHENSION0
Category leader: 92 (A)
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ANALYTICS · RANK #8 OF 29

PostHog is Good to agents.

Discry independently scored how well an AI agent can discover and understand the PostHog 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 · 71/100
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.Fail
.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 · 84/100
Task-oriented descriptionsEndpoints described by what they accomplish, not just their shape.Pass
Realistic examplesRunnable, real-world request/response examples.Partial
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

  • PostHog's llms.txt includes an exceptional 'Instructions for AI Coding Assistants' section with specific guidance on installation (wizard), products to suggest, and API usage patterns — a model for agent-oriented documentation
  • The llms.txt is 272KB which is too large for efficient single-pass agent consumption, despite the quality of its content
  • PostHog has an official MCP server (mcp.posthog.com) and Claude Code plugin — best-in-class agent tooling for analytics
  • Docs support .md URL appending (any doc URL + .md returns markdown) — excellent for programmatic access
  • No .well-known/mcp.json despite having official MCP server; OpenAPI schema accessible at us.posthog.com/api/schema/

What to change

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

  1. 01Create a focused llms.txt under 50KB that covers only the API endpoints and core integration patterns, moving the full index to llms-full.txt
  2. 02Add .well-known/mcp.json at posthog.com pointing to the official MCP server
  3. 03Add AGENTS.md to the PostHog/posthog GitHub repo with the 'Instructions for AI' content from llms.txt
  4. 04Add error recovery guidance for common API errors (invalid API key types, rate limits, query timeouts)

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_keypersonal_api_key✓ Error format documented✓ Rate limits documented✓ Pagination documented✕ Idempotency documented

Personal API key auth for API access (distinct from project API key for event capture). JSON error responses. Rate limits documented. Cursor-based pagination. US and EU cloud endpoints documented (us.i.posthog.com / eu.i.posthog.com).

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