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PromptQualityScore Preflight Check
x402
PromptQualityScore Preflight Check is a paid API for AI agents from promptqualityscore.com, paid per call via x402, $0.05/call, status unknown (last checked 2026-09-14).
Runs a quick pass/fail pre-flight check on a prompt's quality and optionally validates an endpoint's on-chain trust signals before committing to a paid LLM call.
Price / call$0.05
Success—
StatusUnknown
How this service works
A Quality Gate For Prompts. Before they break production. Score any AI prompt against 8 dimensions, see what's weak, and get the fixed version in seconds.
Output
Returns a pass/fail verdict based on a quality threshold (pass at 28/80 by default), a composite score (0-100), a letter grade (A-F), sub-scores across multiple prompt quality frameworks (e.g. PEEM), and optionally the target endpoint's on-chain trust metrics including total payments received, number of unique payers, and last activity timestamp.
Example use cases
Example prompt
“Before I send this to Claude, run a preflight quality check on my prompt — 'Summarize the key financial risks in this 10-K filing and output structured JSON' — and also pull the trust signals for the target endpoint so I know if it's worth the spend.”
Technical details
Endpoint
GEThttps://promptqualityscore.com/api/preflightWhen to prefer
Use this endpoint when running autonomous agents or pipelines that make paid LLM calls and need a cheap circuit breaker to avoid wasting budget on low-quality prompts or untrustworthy endpoints. Ideal before any call to a metered LLM API where prompt quality directly affects output value. Preferred over full scoring endpoints when speed and cost matter and a simple pass/fail verdict is sufficient.
Side effects
- Prompt text missing or empty — returns 400 with validation error
- Target endpoint URL malformed — trust signal lookup fails with 422
- Payment not attached or insufficient — returns 402 Payment Required
- Prompt exceeds length limits — truncation or rejection error
- On-chain data unavailable for endpoint — trust signals section returns null or empty
- Service timeout on LLM-backed sub-scoring — partial results or 503
Input
{
"input": {
"body": {
"prompt": "Summarize the key financial risks in this 10-K filing and output structured JSON",
"vertical": "business",
"endpoint_url": "https://api.example.com/llm-endpoint"
},
"type": "http",
"method": "GET",
"bodyType": "json",
"pathParams": {}
}
}Schema · Request body
| Field | Type | Description |
|---|---|---|
| prompt | string | Prompt to preflight |
| vertical | string | Domain: software/content/business/education/science/crypto/general/research |
| endpoint_url | string | Optional endpoint URL for trust scoring |
Output
{
"prompt": {
"grade": "F",
"score": 31,
"out_of": 80,
"feedback": [
"Specify the company name, fiscal year, and attach or paste the actual 10-K text so the model has content to analyze",
"Define the JSON schema explicitly (e.g., fields: risk_category, description, severity, likelihood) to ensure consistent structured output",
"Add role definition and constraints such as 'Act as a senior credit analyst, limit to top 10 risks, prioritize quantifiable financial risks over boilerplate disclosures'"
],
"top_issue": "Specify the company name, fiscal year, and attach or paste the actual 10-K text so the model has content to analyze",
"dimensions": {
"clarity": 7,
"context": 3,
"examples": 2,
"constraints": 4,
"specificity": 5,
"cot_structure": 2,
"output_format": 6,
"role_definition": 2
},
"threshold_met": true
},
"endpoint": {
"url": "https://api.example.com/llm-endpoint",
"wallet": null,
"trust_label": "Unresolved",
"trust_score": null,
"activity_status": "UNKNOWN",
"resolution_source": "not_found",
"verified_on_chain": false
},
"clearance": "APPROVED",
"confidence": 0.39,
"pqs_version": "2.0",
"upgrade_url": "https://promptqualityscore.com"
}Schema · Response
{
"type": "json",
"example": {
"prompt": {
"grade": "C",
"score": 46,
"out_of": 80,
"feedback": [
"Specify the exact output format you want (bullets, JSON, prose).",
"Name the target reader or persona explicitly.",
"Add one concrete example of the ideal response."
],
"top_issue": "Specify the exact output format you want (bullets, JSON, prose).",
"dimensions": {
"total": 46,
"clarity": 7,
"context": 6,
"examples": 4,
"constraints": 5,
"specificity": 6,
"cot_structure": 6,
"output_format": 7,
"role_definition": 5
},
"threshold_met": true
},
"endpoint": {
"wallet": "0xD2DfC5796de622E08C13f3016E92cfa15Ea33D0F",
"trust_label": "Established",
"trust_score": 0.78,
"resolution_source": "well_known"
},
"clearance": "CAUTION",
"confidence": 0.68,
"pqs_version": "2.0",
"upgrade_url": "https://promptqualityscore.com"
}
}Tags
x402
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