Text levenshtein distance

text-levenshtein-distance · version 1.0.0 · Text · free, no key needed

Compute bounded Levenshtein distance over code points or deterministic grapheme tokens.

Use when you need to: compute levenshtein distance · unit cost edit distance · levenshtein over code points.

Decide before calling

Read the versioned contract and the supported scope below. Reuse text-levenshtein-distance@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 text-levenshtein-distance@1.0.0 for compute levenshtein distance. 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 levenshtein distance
• unit cost edit distance
• levenshtein over code points
• count insertions deletions substitutions
• classic string correction distance
• minimum unit text edits

Not supported

• unbounded text comparison
• locale-sensitive collation
• semantic similarity scoring

Behavior

• Insertion, deletion, and substitution each cost one; equal tokens cost zero.
• token_mode defaults to code_point. grapheme is a simple fixed rule, not UAX #29: a token is a character plus any following combining marks, variation selectors and emoji skin-tone modifiers, anything joined by U+200D stays together, and regional indicators pair up. So a\u200db is one token, and CRLF, Hangul jamo, Indic conjuncts and emoji tag sequences stay split into several.
• The result is the minimum cost from the Wagner-Fischer recurrence.
• Pure bounded dynamic programming; no clock, locale, network, or randomness.

Input

• source (string, required): max length 2048
• target (string, required): max length 2048
• token_mode (one of "code_point", "grapheme", optional)

Output

• distance (integer, required): min 0; max 512000
• source_tokens (integer, required): min 0; max 128
• target_tokens (integer, required): min 0; max 128

Limits

• max input bytes: 2048
• max tokens per text: 128
• max cost: 1000
• max cells: 16641

Example

Request input:

{
  "source": "kitten",
  "target": "sitting"
}

Response:

{
  "result": {
    "distance": 3,
    "source_tokens": 6,
    "target_tokens": 7
  }
}

How to call it

MCP

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

{
  "id": "text-levenshtein-distance",
  "version": "1.0.0",
  "input": {
    "source": "kitten",
    "target": "sitting"
  }
}

HTTP (no key)

curl -X POST https://computefirst.net/v1/tools/text-levenshtein-distance/versions/1.0.0/execute \
  -H "Content-Type: application/json" \
  -d '{"source":"kitten","target":"sitting"}'

The machine-readable contract is at /v1/tools/text-levenshtein-distance/versions/1.0.0.

CLI

node cli.mjs run text-levenshtein-distance 1.0.0 --input input.json --base-url https://computefirst.net

Get the client at /clients/cli/.

Related tools

• Text weighted edit distance: Compute bounded weighted insertion, deletion, and substitution distance over text tokens.
• Text damerau distance: Compute bounded optimal-string-alignment distance with adjacent transpositions.
• Text edit alignment: Return a bounded optimal edit alignment with a documented deterministic tie-break order.
• Text count: Count Unicode code points, UTF-16 code units, UTF-8 bytes, and lines.
• Text diff stats: Summarize a line-record diff as keep, insert, delete, and hunk counts.
• Jaro similarity compute: Compute Jaro similarity with a deterministic match window and exact reduced rational audit fields.