◎ Discry Score
tavily.com
ai · API
A
0 / 100
DISCOVERY0
COMPREHENSION0
Category leader: 99 (A)
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AI · RANK #6 OF 43

Tavily is Agent-Ready to agents.

Discry independently scored how well an AI agent can discover and understand the Tavily 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 · 93/100
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.Pass
AGENTS.mdAn AGENTS.md that tells coding agents how to build on the API.Partial
.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.Pass

Comprehension

55% of score · 100/100
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.Pass

What we found

  • An agent discovering Tavily lands on purpose-built infrastructure: a well-structured llms.txt index, a full llms-full.txt, a canonical agents.md setup guide, and every docs page available as clean Markdown by appending .md — this is reference-quality agent discovery, not an afterthought.
  • The full OpenAPI 3.0.3 spec ('Tavily Search and Extract API') is publicly served through the Mintlify docs, and a valid /.well-known/mcp.json declares the remote MCP server with bearer + OAuth2 auth — two signals that ~98% of scanned APIs fail.
  • Error recovery is best-in-class for agents: when a limit is hit, Tavily returns natural-language instructions telling the agent what happened and how to continue, alongside a documented 429 + retry-after contract — an agent can self-correct without a human in the loop.
  • robots.txt explicitly allows every major AI crawler (GPTBot, ClaudeBot, anthropic-ai, Google-Extended, CCBot, PerplexityBot, ChatGPT-User), so agent-facing content is fully indexable.
  • The one gap: no canonical AGENTS.md in the primary GitHub repo (tavily-mcp ships only a README). The hosted agents.md guide covers the same ground and more, but a coding agent inspecting the repo won't find the conventional file.

What to change

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

  1. 01Add an AGENTS.md to the primary GitHub repos (tavily-mcp, tavily-python, tavily-js) mirroring the hosted agents.md — this is the one discovery check not fully passing and would lift Discovery from 93 to ~100.
  2. 02Publish the OpenAPI spec at a stable, conventional path (e.g. https://docs.tavily.com/openapi.yaml or /openapi.json) in addition to embedding it in the Mintlify endpoint pages, so agents probing standard locations find it without scraping docs.
  3. 03Register the mcp.json / OpenAPI location within the docs sitemap and llms.txt so agents that start from the index can reach machine-readable specs in one hop.
  4. 04Document idempotency/retry-safety semantics explicitly (e.g., whether repeating a search on retry is billed and whether session_id de-duplicates), since agents building retry loops on top of the 429 guidance need to know the billing/side-effect contract.

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 (Bearer token)OAuth 2.0keyless mode Error format documented Rate limits documented Pagination documented Idempotency documented

Execution documentation is exceptional: three auth paths (Bearer API key, OAuth 2.0 for MCP, and a keyless trial mode), machine-parseable JSON errors, and per-environment/per-endpoint rate-limit tables with an explicit retry-after header contract. No pagination or idempotency keys are documented — neither applies to Tavily's request/response search model (results are capped via max_results rather than paged).

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