{"uid":"cap_vT-FKXHxCxYxw1WFZZdyt","slug":"entity-name-fuzzy-matcher-c64cec15","name":"Entity Name Fuzzy Matcher","description":"X402 micropayments server — AI agents pay USDC for OFAC screening, rates, and billing verification.","url":"https://x402helper.xyz/89e509/match/name?utm_source=zero.xyz","method":"GET","headers":{},"bodySchema":{"type":"object","$schema":"https://json-schema.org/draft/2020-12/schema","required":["input"],"properties":{"input":{"type":"object","required":["type","method"],"properties":{"type":{"type":"string","const":"http"},"method":{"enum":["GET"],"type":"string"},"queryParams":{"type":"object","required":["a","b"],"properties":{"a":{"type":"string","default":null,"description":"First string to compare"},"b":{"type":"string","default":null,"description":"Second string to compare"},"type":{"enum":["person","company","address","product","org"],"type":"string","default":null,"description":"Entity type selecting the normalization pipeline (default company)"},"min_score":{"type":"integer","default":null,"description":"Optional match threshold 0-100 (default is the per-type threshold)"}}}},"additionalProperties":false},"output":{"type":"object","required":["type"],"properties":{"type":{"type":"string"},"example":{"type":"object"}}}}},"responseSchema":{"type":"json","example":{"result":{},"success":true}},"example":null,"exampleRequest":null,"tags":["x402"],"displayCostAmount":"0.013","displayCostAsset":"USDC","priceDynamic":false,"priceHint":null,"priceStatus":"priced","priceSource":"probe","requiresHandshake":false,"reviewCount":0,"rating":{"score":"0.00","successRate":"0.00","reviews":0,"stars":null,"state":"unrated"},"availabilityStatus":"unknown","priceObserved":null,"sessionDeposit":null,"pricing":{"kind":"static","summary":"$0.013/call","primary":{"kind":"static","protocol":"x402","network":"base","amountUsd":"0.013","per":"call","confidence":"exact"},"accepted":[{"kind":"static","protocol":"x402","network":"base","amountUsd":"0.013","per":"call","confidence":"exact"}]},"paymentMethods":[{"uid":"pm_JtKUlyXCsEJXECdCrhpyg","protocol":"x402","methodType":"crypto","chain":"base","mode":"charge","costAmount":"0.013","costPer":"request","priority":0,"asset":"0x833589fCD6eDb6E08f4c7C32D4f71b54bdA02913","unit":"request","depositMicros":null,"planRef":null}],"brandName":null,"brandSlug":null,"brandBaseUrl":null,"brandDocsUrl":null,"whatItDoes":"Compares two name strings and returns a fuzzy match score indicating how similar they are, with optional entity-type-specific normalization.","exampleAgentPrompt":"Can you check if 'International Business Machines' and 'IBM Corp' are the same company — run a fuzzy name match between them using the company entity type?","exampleUseCases":[{"title":"Vendor deduplication before invoice processing","prompt":"We have two supplier records — 'Acme Industries LLC' and 'ACME Ind.' — can you check if these are the same company using a fuzzy name match with a minimum score of 80?"},{"title":"KYC person name verification","prompt":"Check whether the name 'Jonathan R. Smith' matches 'Jon Smith' for a person entity — I need to know if these could be the same individual."},{"title":"Organization name normalization check","prompt":"Do a fuzzy match between 'World Health Organisation' and 'WHO - World Health Organization' using the org entity type and tell me the similarity score."}],"resultDescription":"A JSON object with a 'success' boolean and a 'result' object containing the fuzzy match score and match determination between the two input name strings.","failureModes":["Missing required 'a' or 'b' query parameters returns an error","Invalid entity type enum value causes validation failure","Non-string input for name fields may cause unexpected results","Score threshold outside 0-100 range may be rejected","Payment failure via x402 protocol blocks access to the endpoint"],"whenToPreferThis":"Use this endpoint when you need to programmatically compare two name strings — especially for entity types like persons, companies, addresses, or organizations — and want a normalized similarity score with configurable match thresholds. Prefer it over generic string-distance libraries when entity-type-aware normalization (e.g. handling 'LLC', 'Inc', common abbreviations) is important for accuracy.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-11-07T06:47:34.277Z","isFirstParty":false,"canonicalSlug":"entity-name-fuzzy-matcher-c64cec15"}