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Qty — Deterministic Quantity Parser and Unit Converter
x402
Qty — Deterministic Quantity Parser and Unit Converter is a paid API for AI agents from qty.vercel.app, paid per call via x402, $0.002/call, status unknown (last checked 2026-10-12).
Parses messy natural-language quantity expressions (measurements, prices, recipes, sizes, dates) and converts them to structured, deterministic numeric values with units and SI equivalents.
Price / call$0.002
Success—
StatusUnknown
How this service works
Qty converts known units for free and interprets messy quantities, prices, recipes, sizes, and dates in code. It never invents a number.
Output
Returns a JSON object with an array of parsed quantities, each containing the numeric value, label, unit, SI unit, SI value, the formula used to derive the result, confidence score (0–1), any warnings, and the assumptions made during parsing. Also includes links to docs, MCP, OpenAPI spec, and related resources.
Example use cases
Recipe measurement normalization
“I have a recipe that calls for '2 heaping tablespoons of flour' — can you convert that to grams so I can scale it precisely?”
Height conversion for medical form
“My height is listed as 5 feet 11 inches on my US driver's license — what is that in centimeters for a European medical form?”
Price per unit normalization
“One store charges $3.49 per pound and another charges $7.89 per kilogram — can you convert both to price per kilogram so I can compare them?”
Technical details
Endpoint
GEThttps://qty.vercel.app/v1/parse?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need deterministic, traceable quantity parsing with explicit formulas and confidence scores — especially for cooking, pricing, physical measurements, or any context where inventing or hallucinating a number would be harmful. Prefer it over LLM-based conversion when auditability and reproducibility matter, or when inputs are messy natural-language strings that need to be normalized to SI units reliably.
Side effects
- Ambiguous unit string with no resolvable interpretation returns low confidence or error
- Missing required 'input' parameter causes a 400-level error
- Unsupported or invented unit names may return warnings or null results
- Highly contextual quantities without enough information may produce low-confidence results with caveats
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| inputrequired | object | |
| output | object |
Output
Schema · Response
{
"type": "json",
"example": {
"see": {
"mcp": "/mcp",
"docs": "/docs",
"llms": "/llms.txt",
"x402": "/.well-known/x402.json",
"openapi": "/openapi.json",
"llmsFull": "/llms-full.txt",
"agentCard": "/.well-known/agent-card.json"
},
"quantities": [
{
"unit": "cm",
"label": "height",
"value": 180.34,
"reason": null,
"siUnit": "m",
"formula": "5 ft × 0.3048 m + 11 in × 0.0254 m = 1.8034 m; 1.8034 m = 180.34 cm",
"siValue": 1.8034,
"warnings": [],
"confidence": 0.98,
"assumptions": [
"Feet and inches use the international foot (0.3048 m) and the international inch (0.0254 m)."
],
"definitionId": "length.foot.international"
}
]
}
}Tags
x402
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