{"uid":"cap_xLZ4HJnjVqfsQphRNPCng","slug":"trixie-lasagne-entity-matching-ce21db37","name":"Trixie Lasagne Entity Matching","description":"Deterministic cross-dataset entity matching with configurable field-level scoring","url":"https://trixielasagne-api.purplewater-b02e2643.eastus.azurecontainerapps.io/entity/match","method":"POST","headers":{},"bodySchema":{"type":"object","$schema":"https://json-schema.org/draft/2020-12/schema","required":["input"],"properties":{"input":{"type":"object","required":["type","method","bodyType","body"],"properties":{"body":{"properties":{}},"type":{"type":"string","const":"http"},"method":{"enum":["POST","PUT","PATCH"],"type":"string"},"bodyType":{"enum":["json","form-data","text"],"type":"string"}},"additionalProperties":false},"output":{"type":"object","required":["type"],"properties":{"type":{"type":"string"},"example":{"type":"object"}}}}},"responseSchema":null,"example":null,"exampleRequest":null,"tags":["x402"],"displayCostAmount":"0.01","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":"$0.01/call","primary":{"kind":"static","protocol":"x402","network":"base","amountUsd":"0.01","per":"call","confidence":"exact"},"accepted":[{"kind":"static","protocol":"x402","network":"base","amountUsd":"0.01","per":"call","confidence":"exact"}]},"paymentMethods":[{"uid":"pm_QmSB4zJyTaGP1A5K8xRXW","protocol":"x402","methodType":"crypto","chain":"base","mode":"charge","costAmount":"0.01","costPer":"request","priority":0,"asset":"0x833589fCD6eDb6E08f4c7C32D4f71b54bdA02913","unit":"request","depositMicros":null,"planRef":null}],"brandName":null,"brandSlug":null,"brandBaseUrl":null,"brandDocsUrl":null,"whatItDoes":"Deterministically matches entities across two or more datasets using configurable field-level scoring to identify which records refer to the same real-world object","exampleAgentPrompt":"I have two customer lists — one from our CRM and one from our billing system — can you match them up and tell me which records refer to the same person, using name and email as the primary matching fields?","exampleUseCases":[{"title":"CRM and billing system reconciliation","prompt":"We have customer records in our CRM and a separate billing database and I need to know which entries refer to the same person — can you cross-match them using name, email, and phone number as matching fields?"},{"title":"Supplier catalog deduplication across sources","prompt":"I've got two supplier product catalogs from different vendors and I need to figure out which products overlap — match them by product name and SKU and give me a confidence score for each pair."},{"title":"Cross-dataset contact list merging","prompt":"I have a list of event attendees and a newsletter subscriber list and I want to know who appears in both — match them using first name, last name, and company name as the scoring fields."}],"resultDescription":"Returns matched entity pairs across the two input datasets, each pair annotated with a deterministic match score based on the configured field-level weights. The response includes matched record identifiers, per-field similarity contributions, and an overall confidence or match score indicating how strongly the two records are believed to refer to the same real-world entity.","failureModes":["Missing or malformed required 'input' body causes 400 validation error","Incompatible field names between datasets result in zero-score matches or empty results","Misconfigured field weights (e.g. all zero) may produce degenerate scoring","Very large datasets may hit timeout or memory limits on the container","Non-JSON body when bodyType is set to 'json' returns parse error","No overlapping entities results in empty match list with no error signal"],"whenToPreferThis":"Choose this endpoint when you need deterministic, reproducible cross-dataset entity matching with explicit control over which fields contribute to the match score. Prefer it over probabilistic or ML-based record linkage when auditability and consistency matter, or when you need configurable field-level scoring weights rather than black-box similarity. It complements the sibling within-dataset duplicate detection endpoint, which handles intra-dataset deduplication.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-09-16T00:41:36.943Z","isFirstParty":false}