◎ Discry Score
datanexus.com
devtools · API
D
0 / 100
DISCOVERY0
COMPREHENSION0
Category leader: 96 (A)
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DEVTOOLS · RANK #34 OF 39

DataNexus is Poor to agents.

Discry independently scored how well an AI agent can discover and understand the DataNexus 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 · 17/100
OpenAPI specA machine-readable OpenAPI/Swagger spec agents can parse.Partial
llms.txtAn llms.txt index that points agents to the docs that matter.Fail
llms.txt qualityThe llms.txt is focused, current, and well under the size budget.Fail
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.Fail
SitemapA sitemap so agents can enumerate the docs surface.Fail

Comprehension

55% of score · 90/100
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.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 AI crawler looking for DataNexus's docs is blocked at the door: the marketing/docs site ships the default Cloudflare-managed robots.txt that Disallows GPTBot, ClaudeBot, Google-Extended, CCBot, Amazonbot and every other major AI user-agent, with Content-Signal ai-train=no — a striking contradiction for a product whose entire pitch is 'Public Data Intelligence for AI Agents.'
  • Discovery files agents actually look for are absent: no llms.txt, no llms-full.txt, no sitemap.xml, no AGENTS.md in the public repo, and /.well-known/mcp.json 404s on the live domain (the repo carries a .well-known/mcp-manifest.json that is never served at a discoverable path).
  • Where it wins on discovery is the MCP ecosystem: DataNexus is listed across every major registry — Glama (AAA, position 1), Smithery (98/100), plus ToolRank, MCP Index, Socket and Skills Playground — so an agent browsing registries finds it instantly even though crawlers can't reach the site.
  • Comprehension is excellent: tool names follow a rigid {category}_{verb}_{noun} snake_case scheme (security_fetch_cve_detail, nonprofit_fetch_nonprofit_by_ein), descriptions are task-oriented ('single-call SHIP/CAUTION/BLOCK verdict'), and examples use real identifiers (CVE-2021-44228, EIN 46-5734087, patent US10702600B2) that an agent can copy verbatim.
  • The primary doc is the GitHub README, delivered as clean 'AI-Ready Markdown' with tool tables (name / purpose / source / auth), a rate-limit tier table, a documented error-status enum and a 7-example quickstart — genuinely built for agent consumption, which is why comprehension scores an A while discovery scores an F.

What to change

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

  1. 01Stop blocking AI crawlers on the docs site. Replace the default Cloudflare-managed robots.txt with rules that Allow GPTBot, ClaudeBot, Google-Extended and PerplexityBot on / and /docs — an MCP product marketed to AI agents should be the last site to disallow them. This is the single highest-impact discovery fix.
  2. 02Add an llms.txt (and ideally llms-full.txt) at the domain root: a concise, API-focused index linking to /docs, the 7 tool categories, rate limits and error handling. Given the docs are already well-structured, this is a quick win that would lift multiple discovery checks at once.
  3. 03Serve the MCP manifest at the conventional path: expose /.well-known/mcp.json (or ai-plugin.json) on datanexusmcp.com — the repo already has a .well-known/mcp-manifest.json, so it just needs to be published where agents probe for it.
  4. 04Add an AGENTS.md to the datanexusmcp/mcp-server repo so coding agents get first-class setup/usage context, and publish a sitemap.xml covering the homepage, /docs and /use-cases so the (once unblocked) docs pages are discoverable.
  5. 05Deepen workflow and error-recovery docs: turn the quickstart prompts into 2-3 dedicated end-to-end guides that show tool chaining WITH failure handling (e.g. what to do on a DEGRADED or INVALID_PARAMS response, and how to react to the usage/upgrade_hint on rate-limit approach).

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.

Anonymous (no key, free tier)API key via HTTP header (X-DataNexus-Key / X-Api-Key)API key via DATANEXUS_API_KEY env var (npm/stdio) Error format documented Rate limits documented Pagination documented Idempotency documented

Strong for an MCP server: every tool returns structured JSON with a documented status enum (OK / DEGRADED / NOT_FOUND / INVALID_PARAMS), a graceful DEGRADED partial-data pattern instead of hard failures, tiered rate limits (per-IP free, per-key paid) and an in-response usage/upgrade_hint field. Pagination and idempotency are not addressed.

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