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AgentUtility Match Score
x402
AgentUtility Match Score is a paid API for AI agents from x402.agentutility.ai, paid per call via x402, $0.003/call, status unknown (last checked 2026-09-15).
Computes a 0–100 fuzzy similarity score between two values of a specified type (company, name, address, email, phone, or text) using blended Jaro-Winkler, token overlap, and match-key equality.
Price / call$0.003
Success—
StatusUnknown
How this service works
Scores how similar two values are on a 0-100 scale for fuzzy matching and dedup confidence. Compares two values of a given type (company, name, address, email, phone, or text), blending match-key equality, Jaro-Winkler, and token overlap, and reports whether they share a match key. The companion to the match-key endpoints: bucket by key, then rank candidates by score. Deterministic and instant — no external lookups. Use it as a fuzzy match score, string similarity, or record-pair scoring API.
Output
Returns a numeric similarity score from 0 to 100 (higher = more similar), a boolean or flag indicating whether the two values share a match key, and optionally a confidence label. The result is deterministic and computed locally without external lookups.
Example use cases
CRM duplicate detection
“I have two company records: 'International Business Machines' and 'IBM Corp.' — score how similar they are as company names so I can decide whether to merge them.”
Address matching for delivery data
“Score the similarity between '123 Main St, Springfield, IL 62701' and '123 Main Street, Springfield Illinois' as addresses — I need to know if these are likely the same location.”
Contact deduplication pipeline
“I'm deduplicating a contact list. Tell me how similar 'Jonathan P. Smith' and 'Jon Smith' are as person names and whether they share a match key.”
Technical details
Endpoint
POSThttps://x402.agentutility.ai/match-scoreWhen to prefer
Choose this endpoint when you need a fast, deterministic, single-pair similarity score without any external API calls or ML model latency. Ideal as the ranking step after bucketing candidates by match key — first cluster records using a match-key endpoint, then score pairs within each bucket using this endpoint. Prefer it over embedding-based similarity when you need reproducible, rule-based scores for auditability, or when processing high volumes at low cost ($0.003/call).
Side effects
- Unsupported entity type provided — endpoint only accepts: company, name, address, email, phone, text
- Missing one or both values in the request body
- Payment not provided or insufficient USDC — x402 payment required
- Malformed JSON in request body
- Score may be low for highly abbreviated vs. fully spelled-out values despite semantic equivalence
Output
Schema · Response
{
"type": "json",
"example": {
"a": "Acme Inc",
"b": "Acme Incorporated",
"type": "company",
"score": 100,
"same_key": true,
"normalized_a": "acme",
"normalized_b": "acme"
}
}Tags
x402
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