◎ Discry Score
teamwork.com
productivity · API
B
0 / 100
DISCOVERY0
COMPREHENSION0
Category leader: 92 (A)
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PRODUCTIVITY · RANK #10 OF 24

Teamwork is Good to agents.

Discry independently scored how well an AI agent can discover and understand the Teamwork 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 · 74/100
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.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.Pass
.well-known/mcp.jsonA discoverable MCP manifest at a well-known path.Pass
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 · 75/100
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.Partial
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.Partial
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.Partial

What we found

  • An agent can natively integrate without scraping docs at all: Teamwork ships an official MCP server discoverable via /.well-known/mcp.json (valid JSON with five named profiles, OAuth2/bearer auth, and endpoints) and is listed as an official server on both PulseMCP and Glama — a top-tier discovery outcome most APIs fail.
  • An agent looking for AI-readiness signals finds a well-structured llms.txt and an AI-permissive robots.txt (GPTBot, ClaudeBot, Google-Extended, PerplexityBot, OAI-SearchBot all allowed; only the /launchpad login area is blocked) — Teamwork actively courts AI crawlers.
  • The llms.txt is product/marketing-focused (company info, service offerings, sales links) rather than API-focused, so an agent trying to learn the API from it gets no endpoint or auth guidance — it must fall back to the separate apidocs.teamwork.com site.
  • Endpoint reference is genuinely agent-usable: task-oriented descriptions, exhaustively documented query params with allowed values and defaults, and realistic copy-pasteable code samples in Python/PHP/JavaScript with plausible IDs, names, and dates.
  • There is no clean, publicly downloadable OpenAPI/Swagger file — the docs are spec-backed and interactive but the raw spec isn't offered as a download, and the referenced swaggerdocs repo now 404s, so an agent can't ingest a machine-readable contract in one fetch.

What to change

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

  1. 01Publish a downloadable OpenAPI 3.x spec at a stable, public URL (e.g. apidocs.teamwork.com/openapi.json) for V3 — the highest-weighted discovery signal and the fastest way for an agent to ingest the full API contract in one request.
  2. 02Add an API-focused llms.txt (or llms-full.txt) that links to authentication, paging, error codes, and core V3 endpoints — the current llms.txt is product-focused and gives an agent no path into the API surface.
  3. 03Include the apidocs.teamwork.com documentation pages in a sitemap (the current sitemap only covers the marketing site), so agents crawling for API docs can enumerate every endpoint and guide.
  4. 04Add end-to-end, multi-step workflow guides (e.g. 'create project → add tasklist → assign task → log time') with error handling, beyond the current feature-level guides for auth, paging, and custom fields.
  5. 05Tighten the error-codes guide with explicit, per-parameter recovery steps (the current 400/422/500 entries are informal — 'developer fatigue', 'naughty step' — and don't tell an agent which field was wrong or how to fix it).

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.

Basic (base64 API key / username:password)OAuth 2.0 (bearer token) Error format documented Rate limits documented Pagination documented Idempotency documented

Execution characteristics are well documented: Basic and OAuth 2.0 auth with curl examples, a dedicated error-codes guide (400/401/403/404/422/500 with common causes), rate limiting (150 req/min, HTTP 429, X-Rate-Limit-Limit/Remaining/Reset headers with UTC-epoch reset), and thorough pagination docs (page/pageSize params, x-page/x-pages/x-records headers, V3 meta object with hasMore/pageOffset). Idempotency keys are not mentioned.

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