◎ Discry Score
close.com
crm · API
B
0 / 100
DISCOVERY0
COMPREHENSION0
Category leader: 89 (B)
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CRM · RANK #3 OF 13

Close is Good to agents.

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

Discry your API — freeView the docs ↗

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 · 64/100
OpenAPI specA machine-readable OpenAPI/Swagger spec agents can parse.Partial
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.Pass
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 · 94/100
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.Partial
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.Pass

What we found

  • Close has the most agent-aware robots.txt seen in this batch: explicitly includes Content-Signal: ai-train=yes, search=yes, ai-input=yes — a direct opt-in to AI training and agent consumption.
  • The developer docs explicitly address AI agents in the site header: 'For AI agents: a documentation index is available at /llms.txt and /llms-full.txt. Append .md for the markdown version of any page.'
  • llms.txt is well-structured and genuinely API-focused with clear resource descriptions (Leads, Contacts, Opportunities, Tasks) and documentation links to .md and .mdx versions.
  • Official MCP server listed on PulseMCP with OAuth authentication and Streamable HTTP protocol — agents can connect without API key setup.
  • Rate limit documentation includes explicit recovery instructions ('sleep for rate_reset seconds') — exactly the actionable guidance agents need for self-correction.

What to change

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

  1. 01Add llms-full.txt (the site references it but it currently fails to load) with comprehensive API endpoint documentation.
  2. 02Add AGENTS.md to the closeio/closeio-api Python client or closeio/close-api-scripts repos.
  3. 03Publish a publicly accessible OpenAPI spec (the docs reference one but it's not at a standard discoverable path).
  4. 04Add .well-known/mcp.json pointing to the official Close MCP server for standardized discovery.
  5. 05Add idempotency key support 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.

API key (HTTP Basic Auth)OAuth2✓ Error format documented✓ Rate limits documented✓ Pagination documented✕ Idempotency documented

Well-documented API key auth via HTTP Basic Auth and OAuth2 for user-facing apps. JSON error responses with field-level validation details. Rate limits documented with per-endpoint-group enforcement, per-org and per-key limits, and explicit recovery guidance (sleep for rate_reset seconds). Both offset-based and cursor-based pagination documented.

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