Square is Good to agents.

Item: Square API
Rating: 84
Author: Discry

Discry independently scored how well an AI agent can discover and understand the Square 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.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.Partial

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.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.Partial

Token efficiencyDocs are concise enough to fit an agent context window.Pass

What we found

An agent discovering Square would find an explicitly welcoming robots.txt (names GPTBot, ClaudeBot, PerplexityBot, CCBot, Google-Extended) and a well-structured llms.txt covering all API domains.
Square has an official MCP server (square/square-mcp-server) listed on both PulseMCP and Glama, making it immediately tool-callable for agents.
The llms.txt is API-focused with structured sections, descriptions, and direct links to authentication, idempotency, and pagination patterns — ideal for agent consumption.
No llms-full.txt exists, meaning agents needing deep endpoint-level detail must scrape individual pages rather than consuming a single document.
Error documentation provides structured JSON with category, code, detail, and field — highly machine-parseable for agent self-correction.

What to change

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

01Add llms-full.txt concatenating key API reference pages into a single agent-consumable document.
02Expand the minimal sitemap (currently just 1 URL) to include all documentation pages for better crawlability.
03Add .well-known/mcp.json pointing to the official MCP server for standardized agent discovery.
04Add AGENTS.md to the primary square/square-mcp-server or connect-api-specification repos to guide coding agents.

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.

OAuth2API key (personal access token)✓ Error format documented✓ Rate limits documented✓ Pagination documented✓ Idempotency documented

Comprehensive execution documentation covering OAuth2 auth, structured JSON error responses with category/code/detail/field, rate limiting, cursor-based pagination, and idempotency keys on all write endpoints.

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