Damerau-Levenshtein Scorer

x402

Damerau-Levenshtein Scorer is a paid API for AI agents from kihustle.tech, paid per call via x402, $0.002/call, status unknown (last checked 2026-10-10).

Computes the Damerau-Levenshtein edit distance score between two input strings

Price / call$0.002

Seller volume →—

Success—

StatusUnknown

How this service works

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Output

A JSON object containing a computed Damerau-Levenshtein edit distance score for the two input strings (text_a and text_b), along with a status field indicating success or failure of the computation.

Example use cases

Spell-check candidate ranking

“I have a misspelled word 'accomodation' and a candidate correction 'accommodation' — can you score them with Damerau-Levenshtein so I can rank how close the correction is?”

Duplicate record detection

“I'm comparing two customer name entries: 'Jonathan Smith' and 'Jonathon Smyth' — give me a Damerau-Levenshtein score so I can decide if they're the same person.”

Fuzzy search relevance scoring

“Score the similarity between the search query 'pythoon' and the keyword 'python' using Damerau-Levenshtein distance so I know how relevant to surface it.”

Technical details

Endpoint

POSThttps://kihustle.tech/agents/api/v1/damerau-levenshtein-scorer?utm_source=zero.xyz

When to prefer

Choose this endpoint when you need a precise, transposition-aware edit distance metric between two strings and want a lightweight, pay-per-call API rather than running the algorithm locally. Damerau-Levenshtein is preferred over plain Levenshtein when transpositions (swapped adjacent characters) should count as a single edit, making it better for spell-check, OCR correction, and fuzzy deduplication tasks.

Side effects

Missing text_a or text_b fields returns an error
Non-string input types may cause processing failure
Very long strings may time out or return unexpected results
Empty string inputs may return a zero distance trivially

Input

Schema · Request body

Field	Type	Description
text_a	string
text_b	string

Output

Schema · Response

{
  "type": "json",
  "example": {
    "result": "processed",
    "status": "success"
  }
}

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