◎ Discry Score
appwrite.com
infrastructure · API
A
0 / 100
DISCOVERY0
COMPREHENSION0
Category leader: 98 (A)
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INFRASTRUCTURE · RANK #6 OF 68

Appwrite is Agent-Ready to agents.

Discry independently scored how well an AI agent can discover and understand the Appwrite 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 · 90/100
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.Pass
AGENTS.mdAn AGENTS.md that tells coding agents how to build on the API.Pass
.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 · 96/100
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.Pass
Token efficiencyDocs are concise enough to fit an agent context window.Partial

What we found

  • An agent discovering Appwrite finds nearly every signal it looks for: a current OpenAPI 3 spec (v1.9.5, dedicated appwrite/specs repo), a substantive AGENTS.md, an official MCP server listed in public registries, plus llms.txt and an ~8MB llms-full.txt — top-tier discoverability.
  • Error recovery is a standout. The response-codes page maps dozens of typed errors (e.g. user_invalid_credentials, rule_verification_failed → 'check your DNS records') to concrete fixes, and explicitly tells agents to retry transient 429/503 errors with backoff — so an agent can self-correct instead of guessing.
  • Endpoint descriptions across all 462 paths are task-oriented ('Use this endpoint to allow a new user to register…') and naming is perfectly consistent (260 camelCase parameters, zero snake_case) — agents won't trip on casing or interface-only descriptions.
  • Multi-step workflows are first-class: per-product and per-platform quickstarts (auth quick-start, React Native, etc.) walk end-to-end flows rather than documenting endpoints in isolation, so an agent can chain operations from the docs.
  • The weak spot for agents is orientation: llms.txt is 228KB — far over the ~50KB right-size — so an agent loading it as a map ingests a near-site-wide dump, and there is no .well-known/mcp.json to auto-advertise the (existing) MCP server.

What to change

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

  1. 01Trim llms.txt to a concise (<50KB) API-focused index that links out to llms-full.txt for depth, so agents get a right-sized orientation map instead of a 228KB dump.
  2. 02Add inline request/response examples to the OpenAPI spec (it currently carries zero requestBody examples), so agents parsing the spec directly get realistic values without scraping the rendered reference pages.
  3. 03Publish a .well-known/mcp.json on appwrite.io pointing to the official Appwrite MCP server, so agents can auto-discover the MCP endpoint straight from the domain.
  4. 04Add explicit AI-crawler allow directives (GPTBot, ClaudeBot, anthropic-ai, Google-Extended) to robots.txt — AI bots are currently only permitted implicitly via User-agent:*.

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 keyJWTAccount session (cookie)OAuth2✓ Error format documented✓ Rate limits documented✓ Pagination documented✕ Idempotency documented

Execution characteristics are thoroughly documented: JSON errors carry an HTTP status, a machine-parseable `type`, message and version; rate limits are documented per service plus a dedicated page with 429 retry-with-backoff guidance; cursor-based pagination is documented via the Query API. No idempotency-key mechanism is documented, though caller-supplied unique IDs provide a partial create-once safeguard.

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