NLP that extracts mentions of clinical concepts from text, gives access to clinical ontology
Full LLM thinking from the 4-phase benchmark pipeline.
1/3 tests passed
| Test | Endpoint | Status | Latency |
|---|---|---|---|
| website_uptime | GET / | 200 | 535ms |
| robots_txt | GET /robots.txt | 404 | 127ms |
| llms_txt | GET /llms.txt | 404 | 127ms |
Prowl{
"overall": 25,
"dimensions": {
"token_efficiency": 2.0,
"first_try_success": 1.0,
"response_parseability": 1.0,
"error_clarity": 2.0,
"doc_quality": 1.0,
"auth_simplicity": 1.0,
"latency": 5.5,
"consistency": 3.0
},
"pricing_normalized": {},
"issues": [
"No platform information provided — capabilities, pricing, and documentation are all empty or missing.",
"robots.txt and llms.txt returning 404 — no structured AI-readable documentation available.",
"No clear value proposition or onboarding path for agents.",
"Authentication method and developer experience completely unknown."
],
"recommendations": [
"Provide comprehensive platform documentation, including an llms.txt file for AI agents.",
"Define capabilities and pricing model clearly in both human-readable and machine-readable formats.",
"Implement a robots.txt and create a dedicated AI agent onboarding flow.",
"Add a getting started guide with step-by-step instructions for agent integration."
]
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