{"uid":"cap_S5RdW11nXlyQVRw5TlBk2","slug":"entity-resolve-91d13734","name":"entity-resolve","description":"Fuzzy-dedupe a list of records into clusters of likely-duplicate entities. Blocks by normalized token prefix, scores with Jaro-Winkler + token-set matching (exact on email/phone), unions matches above threshold, and returns a merged canonical record per cluster with a confidence score. Deterministic, no LLM calls.","url":"https://entity-resolve.x402supply.com/mcp","method":"POST","headers":{},"bodySchema":null,"responseSchema":null,"example":null,"exampleRequest":null,"tags":["x402"],"displayCostAmount":"0.02","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":"$0.02/call","primary":{"kind":"static","protocol":"x402","network":"base","amountUsd":"0.02","per":"call","confidence":"exact"},"accepted":[{"kind":"static","protocol":"x402","network":"base","amountUsd":"0.02","per":"call","confidence":"exact"}]},"paymentMethods":[{"uid":"pm_hDheunJKsEFd66-UqH7Fs","protocol":"x402","methodType":"crypto","chain":"base","mode":"charge","costAmount":"0.02","costPer":"request","priority":0,"asset":"0x833589fCD6eDb6E08f4c7C32D4f71b54bdA02913","unit":"request","depositMicros":null,"planRef":null}],"brandName":null,"brandSlug":null,"brandBaseUrl":null,"brandDocsUrl":null,"whatItDoes":"Fuzzy-deduplicates a list of records into clusters of likely-duplicate entities, returning a merged canonical record per cluster with a confidence score.","exampleAgentPrompt":"Take this list of 500 customer records — some have slightly different spellings of the same name or phone number — and cluster them into groups of likely duplicates, giving me a merged canonical record and confidence score for each cluster.","exampleUseCases":[{"title":"CRM deduplication before campaign launch","prompt":"I have a CSV export of 2,000 leads from our CRM with a lot of duplicates — same person with slightly different name spellings or email variations. Can you run it through entity resolution and give me the deduplicated canonical list with confidence scores so I know which merges to trust?"},{"title":"Merging vendor lists from two acquisitions","prompt":"We just merged two companies and both had separate vendor databases. Can you take both lists, fuzzy-match them by company name and phone number, and cluster the ones that are clearly the same vendor so we can eliminate the duplicates?"},{"title":"Cleaning scraped business directory data","prompt":"I scraped business listings from a few different directories and there are tons of duplicate entries with slightly different names or addresses. Can you cluster these into groups of likely-same businesses and return one canonical record per business along with how confident you are in each merge?"}],"resultDescription":"A list of entity clusters, each containing the matched records grouped together, a merged canonical record synthesizing the best available field values, and a confidence score (0–1) indicating how certain the algorithm is that the records refer to the same entity. No LLM inference is involved — results are deterministic based on Jaro-Winkler + token-set scoring and union-find clustering above a configurable threshold.","failureModes":["Records with no overlapping tokens may not be blocked together and will be missed","Very short or ambiguous names may produce false positives or false negatives","Missing email/phone fields reduce match precision since those fields get exact matching","Threshold misconfiguration can cause over-merging or under-merging of clusters","Malformed input records (missing required fields) may cause processing errors","Extremely large record lists may exceed per-call size limits"],"whenToPreferThis":"Choose this endpoint when you need deterministic, auditable entity deduplication without LLM involvement — especially when reproducibility matters and you can't risk non-deterministic AI output. Ideal for cleaning CRM exports, merging data from multiple sources, or preprocessing datasets before analytics. Prefer this over LLM-based matching when you need consistent results across repeated runs or when cost predictability is important at $0.02 per call.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-09-15T06:35:49.483Z","isFirstParty":false}