{"uid":"cap_KhtMG0LeN4h8TiV5ODJc9","slug":"jev-filter-3a29252b","name":"jev-filter","description":"Filter up to 25 records against up to 10 yes/no criteria you supply. Each record comes back with keep / review / drop, a reason naming the criterion it failed, a 0-10 score and the per-criterion probabilities. Never drops a record it could not read or could not judge: those come back as review or keep, flagged. Built on a decision model rather than a chat model, so it is a flat per-call price with no token accounting on your side.","url":"https://x402-production-0f93.up.railway.app/v1/filter?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"mode":{"enum":["all","any","score"],"type":"string","default":"all","description":"all: every criterion must hold, each as its own gate so a drop is attributable. any: at least one must hold. score: the weighted mean must clear the threshold."},"records":{"type":"array","items":{"type":"object"},"maxItems":25,"minItems":1,"description":"Any shape. At most 4000 characters per record."},"criteria":{"type":"array","items":{"type":"object","required":["key","question"],"properties":{"key":{"type":"string","pattern":"^[a-z][a-z0-9_]*$","description":"Short snake_case name. Appears in the result as failed_<key>."},"weight":{"type":"number","description":"Only used when mode is \"score\". Defaults to 1."},"question":{"type":"string","description":"One yes/no statement about a single record, phrased so that TRUE means you want the record. Narrow and factual works; 'is this a good fit' does not."}}},"maxItems":10,"minItems":1},"dropBelow":{"type":"number","description":"score mode only. Below this a record is dropped; between this and threshold it is marked review. Defaults to half the threshold."},"threshold":{"type":"number","description":"In all/any mode, how certain one criterion must be to count as true (0-1, default 0.5). In score mode, the 0-10 score a record must reach (default 5)."}}},"responseSchema":{"type":"json","example":{"meta":{"items":2,"bundle":"dataset-filter","failures":0,"elapsed_ms":640,"bundle_version":"0.1.0","model_cost_usd":0.00012},"results":[{"id":"1","score":9.81,"reason":"all_criteria_met","verdict":"keep","rationale":"jev@openrouter: keep; reason all_criteria_met; in_europe 0.99, makes_things 0.97","confidence":"high","rules_applied":[]},{"id":"2","score":4.9,"reason":"failed_makes_things","verdict":"drop","rationale":"jev@openrouter: drop; reason failed_makes_things; in_europe 0.98, makes_things 0.03","confidence":"high","rules_applied":["require_makes_things"]}]}},"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_877rUbshF9w7F9p36nZBL","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":"Filters or scores a batch of records against user-supplied yes/no criteria using LLM-based typed decisions, with three modes: all, any, or weighted score.","exampleAgentPrompt":"Filter these 10 prospect records in 'score' mode against two criteria — 'is the company based in Europe' (weight 2) and 'does the company make physical products' (weight 1) — and drop anything scoring below 4 out of 10.","exampleUseCases":[{"title":"ICP lead scoring for sales pipeline","prompt":"Score these 20 inbound leads against my ideal customer profile: they must be a B2B SaaS company with more than 50 employees and based in North America. Use score mode with a threshold of 6 and drop anything below 3."},{"title":"Applicant screening against job criteria","prompt":"Filter these 15 job applicant records and keep only those where the candidate has at least 5 years of experience AND has a background in machine learning. Use all-criteria mode with a confidence threshold of 0.6."},{"title":"E-commerce product catalog filtering","prompt":"I have 25 product records and I want to keep only the ones that are in stock AND have a customer rating above 4 stars. Run them through in 'all' mode and tell me which ones fail and why."}],"resultDescription":"A JSON object containing a meta block (item count, elapsed time, model cost) and a results array where each record gets a verdict (keep/drop/review), a 0–10 score, a reason string naming the failing criterion (e.g. failed_makes_things), a human-readable rationale, and a confidence level (high/medium/low).","failureModes":["Record exceeds 4000 character limit — record rejected or truncated","More than 25 records submitted — request rejected with validation error","More than 10 criteria supplied — request rejected","Criterion question is too vague or subjective — low-confidence verdicts returned","Invalid criterion key format (not snake_case) — schema validation error","Payment not completed via x402 — 402 Payment Required response","LLM inference timeout — elapsed_ms high, potential partial results"],"whenToPreferThis":"Choose this endpoint when you need transparent, attributable, per-criterion filtering of structured records at low cost ($0.01/call for up to 25 records). It is ideal when you want to know exactly which criterion caused a record to be dropped (via failed_<key> reasons), need a review tier for borderline records, or want weighted ICP scoring rather than binary pass/fail. Prefer it over bespoke LLM prompts when you need structured, consistent verdicts across a batch with deterministic schema output.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-02T18:31:47.228Z","isFirstParty":false,"canonicalSlug":"jev-filter-3a29252b"}