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Text Similarity Fuzzy Rank
x402
Text Similarity Fuzzy Rank is a paid API for AI agents from text-similarity.x402tookit.com, paid per call via x402, $0.02/call, status unknown (last checked 2026-10-21).
Ranks up to 50 candidate strings against a query using Jaro-Winkler, Levenshtein, Dice, or cosine similarity, returning results sorted best-first with scores.
Price / call$0.02
Success—
StatusUnknown
How this service works
Rank up to 50 candidates against a query by Jaro-Winkler, Levenshtein, Dice or cosine, best first, with scores.
Output
A JSON object containing the original query, the scorer used, and a ranked array of candidates each with their rank number, original index, similarity score, and the candidate text — sorted best-first. Also includes metadata like total candidate count, how many were returned, and whether the response was degraded.
Example use cases
Product name deduplication pipeline
“I have a master product name 'Wireless Noise-Cancelling Headphones' and a list of 30 user-submitted variants — rank them all by cosine similarity so I can see which ones are basically the same and flag the ones scoring below 0.5.”
Autocomplete candidate ranking
“A user typed 'Shakespear' as a search query — here are 20 book titles they might have meant, can you rank them by Jaro-Winkler similarity so I can surface the best autocomplete suggestions first?”
Address matching for data enrichment
“I need to match the address '123 Elm Street' against a list of 15 stored address strings and get them ranked by Levenshtein similarity, returning only the top 5 closest matches.”
Technical details
Endpoint
GEThttps://text-similarity.x402tookit.com/fuzzy-rank?utm_source=zero.xyzWhen to prefer
Use this endpoint when you need to rank multiple candidate strings against a single query in one call, rather than comparing pairs one at a time. It is ideal for autocomplete ranking, fuzzy search result sorting, deduplication pipelines, and name matching tasks where you want to quickly identify the best matches from a list. Prefer Jaro-Winkler for short names and typos, Levenshtein for edit-distance-based matching, Dice for character n-gram overlap, and cosine for longer multi-word texts. Choose this over individual pairwise comparison endpoints when you have a batch of candidates to evaluate simultaneously.
Side effects
- Missing required 'query' or 'candidates' parameter returns a validation error
- More than 50 candidates submitted causes rejection or truncation
- Candidate or query strings exceeding 200 characters may be rejected
- Invalid scorer enum value returns an error
- minScore outside 0–1 range causes a validation error
- Malformed candidates string (not pipe-separated, newline-separated, or JSON array) causes parse failure
- Network timeout if candidates list is very large near the 50-item limit
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| inputrequired | object | |
| output | object |
Tags
x402
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