◎ Discry Score
zoom.com
communication · API
B
0 / 100
DISCOVERY0
COMPREHENSION0
Category leader: 98 (A)
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COMMUNICATION · RANK #13 OF 24

Zoom is Good to agents.

Discry independently scored how well an AI agent can discover and understand the Zoom 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 · 88/100
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.Pass
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

  • Zoom provides an exceptionally comprehensive llms.txt (204KB) with API catalog, MCP catalog, and full documentation index — one of the most thorough agent discovery setups seen
  • robots.txt includes Content-Signal: ai-train=yes explicitly welcoming AI consumption, and lists llms.txt as a Sitemap entry
  • The llms.txt includes a dedicated MCP Catalog section listing Zoom Workplace MCP alongside documentation — forward-looking agent infrastructure
  • OpenAPI specs are publicly available on GitHub at zoom/api with JSON format
  • An official Zoom Workspace MCP server exists on PulseMCP alongside many community implementations on all three major registries

What to change

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

  1. 01Consider splitting the 204KB llms.txt into a focused llms.txt (<50KB) and llms-full.txt for comprehensive coverage
  2. 02Add .well-known/mcp.json pointing to the official Zoom Workspace MCP server
  3. 03Add AGENTS.md to the zoom GitHub org with context for coding agents
  4. 04Add llms-full.txt with the complete API documentation in markdown format
  5. 05Create more realistic code examples with actual values across multiple languages

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.

OAuth2JWT (deprecated)Server-to-Server OAuth✓ Error format documented✓ Rate limits documented✓ Pagination documented✕ Idempotency documented

Zoom uses OAuth 2.0 and Server-to-Server OAuth. JWT deprecated. JSON error responses with code and message. Rate limits documented per endpoint type. Cursor-based pagination.

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