Notion is Good to agents.
Discry independently scored how well an AI agent can discover and understand the Notion API from what’s public — not whether it’s usable. Below: every signal we checked, what’s costing the score, and what to change.
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 · 83/100Comprehension
55% of score · 88/100What we found
- Notion achieves strong scores across both dimensions (87/90) with llms.txt that is API-focused and right-sized, llms-full.txt (1.4MB), OpenAPI 3.1.0 spec, and multi-step workflow guides for building integrations
- The status codes page lists errors with example messages and context (e.g., object_not_found includes the connection name) but lacks explicit recovery steps beyond 'try again' — earning partial on errorRecoveryGuidance
- Multi-step workflow documentation is solid: guides cover building full integrations from auth setup through database queries and page manipulation end-to-end
- Response examples still use generic placeholders (<string>) rather than realistic values, which impacts agent comprehension of expected output shapes
- No AGENTS.md in makenotion repos despite having an official MCP server, agents SDK, and skills repo
What to change
Prioritized by impact on discoverability. You (or your docs platform) deploy these — Discry never touches your API.
- 01Add AGENTS.md to makenotion/notion-sdk-js or makenotion/notion-mcp-server with coding agent context
- 02Enhance error documentation with explicit recovery steps: what to DO on rate_limited (check Retry-After), object_not_found (verify sharing permissions), validation_error (check which field failed)
- 03Serve .well-known/mcp.json from developers.notion.com (not just notion.so)
- 04Replace <string> placeholders in response examples with realistic values
- 05Add idempotency key documentation for write operations
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.
Bearer token auth for internal connections and full OAuth 2.0 for public connections. JSON error responses with structured codes and messages. Rate limit of 3 req/s with Retry-After header. Cursor-based pagination with start_cursor and page_size. No idempotency key support.