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Qty Batch Unit Conversion & Quantity Parsing
x402
Qty Batch Unit Conversion & Quantity Parsing is a paid API for AI agents from qty.vercel.app, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-12).
Batch-converts units and parses messy quantities, prices, recipe amounts, sizes, and dates deterministically without inventing numbers
Price / call$0.01
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 array of results, one per input item. Each result includes an ok flag, the item index, and either a quantities array with parsed numeric values and canonical units, or an error object with a code (e.g. 'unparsed') if the input could not be interpreted. Also includes a see map with links to docs, MCP, OpenAPI, and agent card.
Example use cases
Recipe ingredient normalization
“I'm building a recipe app and need to normalize these ingredient amounts — '1 cup', '250 grams', '2 tablespoons', and '3 oz' — into consistent metric units all at once.”
Product size parsing from listings
“I have a bunch of product descriptions with sizes like '10" x 5" x 3"', '1.5L', and '500cc' scraped from a marketplace — can you parse all of them into structured dimensions in one go?”
Price string interpretation
“Can you parse these price strings for me: '$3.49/lb', '€12 per kg', and '¥500 per 100g' — I need the numeric values and units separated so I can compare them.”
Technical details
Endpoint
GEThttps://qty.vercel.app/v1/batch?utm_source=zero.xyzWhen to prefer
Choose this endpoint when you need deterministic, non-hallucinated unit conversion or quantity parsing across multiple values in a single call. Ideal for agents processing recipe ingredients, product dimensions, price strings, or any heterogeneous quantity data where accuracy and reliability matter more than generative interpretation. Prefer over LLM-based quantity extraction when you cannot afford invented numbers.
Side effects
- Input string is too ambiguous or malformed — returns ok:false with error code 'unparsed'
- Missing required 'input' field returns a validation error
- Unknown or unsupported unit type may result in unparsed error
- Batch items with mixed parseable and unparseable entries return mixed ok/error per index
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"
},
"results": [
{
"ok": true,
"index": 0,
"quantities": []
},
{
"ok": false,
"error": {
"code": "unparsed"
},
"index": 1
}
]
}
}Tags
x402
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