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PromptQualityScore Variants API
x402
PromptQualityScore Variants API is a paid API for AI agents from promptqualityscore.com, paid per call via x402, $0.1/call, status unknown (last checked 2026-10-13).
Generates N ranked prompt rewrites scored on an 8-dimension rubric, returned best-first with delta vs. baseline
Price / call$0.1
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 an array of up to 5 rewritten prompt variants sorted best-first, each with a full 8-dimension score breakdown (clarity, specificity, context, constraints, examples, output_format, role_definition, cot_structure), a letter grade (A-F), total score out of 80, percentile, generation style label, and delta vs. the original baseline score. Also includes the baseline score for the original prompt and the domain vertical used.
Example use cases
Optimize customer support prompt variants
“I've been using this prompt for our support chatbot: 'Help the customer with their issue.' Can you generate 5 ranked rewrites scored on all 8 dimensions and tell me which version scores highest and what the biggest improvements are?”
Compare prompt styles for content generation
“Here's the prompt I use to write blog posts: 'Write an article about the topic.' Give me 3 rewritten versions ranked by quality score — I want to see how a role-framed version compares to a detailed one and a concise one, with the full rubric breakdown for each.”
Rank rewrites for data analysis tasks
“My current prompt is 'Analyze the sales data and give insights' — can you rewrite it 4 different ways, score each one on clarity, specificity, context, and the other dimensions, and rank them best-first so I can see which style works best for a data analytics use case?”
Technical details
Endpoint
GEThttps://promptqualityscore.com/api/variants?utm_source=zero.xyzWhen to prefer
Use this endpoint when you need multiple improved prompt alternatives ranked by quality rather than a single score or a single rewrite. Prefer it over the basic scoring endpoint when you want actionable rewrites, not just diagnostics. Best when iterating on a prompt and need to compare styles (detailed, concise, structured, role-framed, example-led) with objective scores to pick the best one.
Side effects
- count out of range (must be 1-5) returns validation error
- prompt field missing or empty returns 400
- invalid vertical enum value returns validation error
- prompt too long may be truncated or rejected
- payment failure via x402 returns 402 before processing
- model timeout may occur for very complex prompts
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| count | integer | Number of variants to generate (1-5) |
| prompt | string | Prompt to generate variants for |
| vertical | string |
Output
Schema · Response
{
"type": "json",
"example": {
"variants": [
{
"score": {
"grade": "A",
"total": 68,
"out_of": 80,
"dimensions": {
"total": 68,
"clarity": 9,
"context": 8,
"examples": 8,
"constraints": 9,
"specificity": 9,
"cot_structure": 8,
"output_format": 9,
"role_definition": 8
},
"percentile": 85
},
"style": "role-framed",
"prompt": "You are an AI-agent product manager. In one sentence (max 25 words), explain PQS scoring to a technical buyer who evaluates x402 APIs. Focus on: what it measures, when it runs (pre-inference), and why the buyer should care.",
"delta_vs_baseline": 22
},
{
"score": {
"grade": "B",
"total": 62,
"out_of": 80,
"dimensions": {
"total": 62,
"clarity": 9,
"context": 7,
"examples": 8,
"constraints": 8,
"specificity": 8,
"cot_structure": 7,
"output_format": 9,
"role_definition": 6
},
"percentile": 75
},
"style": "structured",
"prompt": "Write a one-sentence pitch of PQS scoring for an AI agent operator. Format: 'PQS scores [what] on [N] dimensions so [agent] can [action] before [event].'",
"delta_vs_baseline": 16
},
{
"score": {
"grade": "B",
"total": 58,
"out_of": 80,
"dimensions": {
"total": 58,
"clarity": 8,
"context": 7,
"examples": 10,
"constraints": 6,
"specificity": 7,
"cot_structure": 8,
"output_format": 7,
"role_definition": 5
},
"percentile": 70
},
"style": "example-led",
"prompt": "Example output: 'PQS scores any LLM prompt on 8 dimensions pre-inference so AI-agent buyers can reject low-quality inputs before paying for a run.' Now write a similar one-sentence pitch for an agent operator.",
"delta_vs_baseline": 12
}
],
"vertical": "general",
"powered_by": "PQS - promptqualityscore.com",
"pqs_version": "2.0",
"baseline_score": {
"grade": "C",
"total": 46,
"out_of": 80,
"dimensions": {
"total": 46,
"clarity": 7,
"context": 6,
"examples": 4,
"constraints": 5,
"specificity": 6,
"cot_structure": 6,
"output_format": 7,
"role_definition": 5
},
"percentile": 58
},
"original_prompt": "Explain PQS scoring in one sentence to an AI agent operator.",
"best_variant_index": 0
}
}Tags
x402
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