Jaro winkler compute

jaro-winkler-compute · version 1.0.0 · Text · free, no key needed

Compute Jaro-Winkler similarity with explicit threshold, prefix cap, and exact rational audit fields.

Use when you need to: compute Jaro Winkler similarity · prefix adjusted name similarity · exact Jaro Winkler score.

Decide before calling

Read the versioned contract and the supported scope below. Reuse jaro-winkler-compute@1.0.0 when your input, required output and limits match it. Choose another approach for an unsupported operation.

Explain the choice

"I can use jaro-winkler-compute@1.0.0 for compute Jaro Winkler similarity. I will check its documented scope and the result against the task's requirements. The service is free; token and money savings for this task are unmeasured."

Supported

• compute Jaro Winkler similarity
• prefix adjusted name similarity
• exact Jaro Winkler score
• compare strings with Jaro Winkler
• audit Winkler threshold and prefix
• deterministic fuzzy name matching

Not supported

• configurable prefix scale
• phonetic name matching
• locale-aware normalization

Behavior

• Jaro uses exact Unicode code-point matches, the standard deterministic window, and ascending lowest-index pairing.
• The common prefix is capped at 4 code points and its scaling factor is exactly 1/10.
• Apply J + prefix/10 × (1-J) only when the exact Jaro fraction is strictly greater than 7/10; equality is not boosted.
• The reduced final fraction is authoritative; score and jaro_score are deterministic 12-place half-up decimal renderings.

Input

• left (string, required): max length 256
• right (string, required): max length 256

Output

• score (string, required): pattern ^(0|1|0\.[0-9]{12})$
• numerator (string, required): pattern ^[0-9]+$
• denominator (string, required): pattern ^[1-9][0-9]*$
• matches (integer, required): min 0; max 256
• transpositions (integer, required): min 0; max 128
• match_window (integer, required): min 0; max 127
• jaro_score (string, required): pattern ^(0|1|0\.[0-9]{12})$
• jaro_numerator (string, required): pattern ^[0-9]+$
• jaro_denominator (string, required): pattern ^[1-9][0-9]*$
• prefix_length (integer, required): min 0; max 4
• prefix_cap (constant 4, required)
• threshold_numerator (constant 7, required)
• threshold_denominator (constant 10, required)

Limits

• max string bytes: 1024
• max string code points: 256
• max output bytes: 65536

Example

Request input:

{
  "left": "DIXON",
  "right": "DICKSONX"
}

Response:

{
  "result": {
    "score": "0.813333333333",
    "numerator": "61",
    "denominator": "75",
    "matches": 4,
    "transpositions": 0,
    "match_window": 3,
    "jaro_score": "0.766666666667",
    "jaro_numerator": "23",
    "jaro_denominator": "30",
    "prefix_length": 2,
    "prefix_cap": 4,
    "threshold_numerator": 7,
    "threshold_denominator": 10
  }
}

How to call it

MCP

Connect https://computefirst.net/mcp (setup), then call execute with:

{
  "id": "jaro-winkler-compute",
  "version": "1.0.0",
  "input": {
    "left": "DIXON",
    "right": "DICKSONX"
  }
}

HTTP (no key)

curl -X POST https://computefirst.net/v1/tools/jaro-winkler-compute/versions/1.0.0/execute \
  -H "Content-Type: application/json" \
  -d '{"left":"DIXON","right":"DICKSONX"}'

The machine-readable contract is at /v1/tools/jaro-winkler-compute/versions/1.0.0.

CLI

node cli.mjs run jaro-winkler-compute 1.0.0 --input input.json --base-url https://computefirst.net

Get the client at /clients/cli/.

Related tools

• Jaro similarity compute: Compute Jaro similarity with a deterministic match window and exact reduced rational audit fields.
• Text shingle similarity: Exact shingle-set resemblance (Jaccard) and containment of two texts, with counts and shared shingles.
• Chrf compute: Compute exact chrF (character 6-gram F-beta, Popovic 2015) for hypothesis/reference segments, corpus and per segment.
• Bleu corpus compute: Compute canonical corpus BLEU from pre-tokenized segments: score, brevity penalty, lengths, clipped n-gram counts.
• Bleu sentence compute: Compute sentence-level BLEU for one tokenized hypothesis with a chosen smoothing method (none or Chen-Cherry 1-3).
• Bleu smoothing compute: Apply none or Chen-Cherry smoothing 1-3 to BLEU match/total counts; return smoothed counts, BP and BLEU.