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DopamineDesk Agent Readiness Audit
x402
DopamineDesk Agent Readiness Audit is a paid API for AI agents from ai-data-marketplace-1042299154756.us-central1.run.app, paid per call via x402, $0.025/call, status unknown (last checked 2026-09-15).
Audits a public domain for AI-agent readiness, returning a score, grade, and actionable recommendations for improving agent/crawler discoverability.
Price / call$0.025
Success—
StatusUnknown
How this service works
Run an agent security and AI discovery readiness audit for a public site, checking the manifests and files agents use to discover, understand, and purchase. Returns a deterministic score, evidence, and repair recommendations.
Output
Returns a JSON object with the audited domain, a boolean success flag, an integer readiness_score (0–100), a readiness_grade string (e.g. 'poor', 'fair', 'good'), counts of checks_passed and checks_requested, and an array of human-readable recommendations for improving agent discoverability (e.g. publishing /llms.txt).
Example use cases
Pre-launch AI discoverability check
“Before we go live with our new product site, run an agent readiness audit on acme-corp.com and list all the recommendations so we can fix them before launch.”
Competitive benchmark for AI readiness
“I want to compare how agent-ready my site is versus a competitor — can you audit both mysite.io and rivalsite.io and tell me their readiness scores and grades?”
Ongoing monitoring for llms.txt compliance
“Check whether openai.com has published llms.txt and passes the standard agent readiness checks — give me the full score breakdown and any recommendations.”
Technical details
Endpoint
GEThttps://ai-data-marketplace-1042299154756.us-central1.run.app/api/v1/agent_readiness_auditWhen to prefer
Use this endpoint when you need a structured, scored assessment of how well a public website supports AI agent discovery and crawling — especially when you want actionable, itemised recommendations. Prefer this over generic security scanners when the goal is specifically agent/LLM-readiness (e.g. llms.txt presence, crawler-friendly signals). It is also the right choice when you want a quick, pay-per-use audit without setting up a full monitoring stack.
Side effects
- Missing or invalid 'domain' query parameter returns an error response
- Non-existent or unreachable domain may result in low scores or a failure flag
- Domain with path included (instead of bare hostname) may be rejected or mis-audited
- Payment not settled via x402 results in HTTP 402 and no data returned
- Rate limiting or infrastructure issues may return 5xx errors
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| inputrequired | object | |
| output | object |
Output
Schema · Response
{
"type": "json",
"example": {
"caveat": "This is a deterministic public-discovery audit. It does not measure model rankings, guarantee crawler inclusion, or prove agent traffic.",
"domain": "stripe.com",
"source": "live public web fetch with private-network blocking",
"success": true,
"evidence": "[object with keys: homepage, robots_txt, sitemap, llms_txt, x402_manifest, openapi; full example in /openapi.json]",
"fetched_at": "2026-08-07T08:16:35.812Z",
"checks_passed": 4,
"readiness_grade": "poor",
"readiness_score": 37,
"recommendations": "[array; full example in /openapi.json]",
"checks_requested": 12,
"marketplace_metadata": "[object with keys: data_mode, billable, availability, source; full example in /openapi.json]"
}
}Tags
x402
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