{"uid":"cap_ylI5ncsinX3O9bp-FvLfG","slug":"arbiter-semantic-ranking-ae946b98","name":"ARBITER Semantic Ranking","description":"ARBITER deterministic semantic control-flow and arbitration: measure and rank a fixed candidate field against current state, intent, or perspective.","url":"https://api.grip.fyi/v1/compare","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"query":{"type":"string","description":"Current state, intent, question, or perspective."},"top_k":{"type":"integer","default":3,"minimum":1},"use_freq":{"type":"boolean","default":true},"candidates":{"type":"array","items":{"type":"string"},"minItems":1,"description":"Fixed candidate field to measure and rank."}}},"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_ngOihhmvIOXqKrhsL_x8f","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":"Ranks a fixed list of candidate strings against a query representing current state, intent, or perspective using deterministic semantic scoring.","exampleAgentPrompt":"Rank these 5 response strategies — ['apologize and refund', 'escalate to manager', 'offer store credit', 'request more info', 'close the ticket'] — against the current intent 'angry customer reporting a missing order', and return the top 3 most appropriate options.","exampleUseCases":[{"title":"Routing agent decisions by context","prompt":"I have these possible next actions for my support bot: ['check order status', 'initiate return', 'connect to human', 'send FAQ link', 'schedule callback']. Rank them against the current user message 'my package never arrived and I want my money back' and give me the top 2."},{"title":"Selecting best prompt template","prompt":"I have 4 prompt templates for different writing styles — ['concise bullet points', 'formal paragraph', 'casual conversational', 'step-by-step numbered list'] — which one best fits the intent 'explain a technical error to a non-technical user'? Return the top 1."},{"title":"Prioritizing knowledge base articles","prompt":"Given a user's question 'how do I reset my 2FA device?', rank these 6 help articles — ['Account security overview', 'Resetting your password', 'Two-factor authentication setup', 'Billing and payments', 'Logging in on a new device', 'Contacting support'] — and show me the top 3 most relevant ones."}],"resultDescription":"Returns a ranked list of up to top_k candidate strings ordered by their deterministic semantic relevance to the query, along with their associated scores or rankings indicating closeness of fit to the stated intent or state.","failureModes":["Empty candidates array returns a validation error","top_k larger than candidates array length may return fewer results","Highly ambiguous or very short query strings may produce low-confidence rankings","Non-string entries in candidates array may cause schema validation failures","Network timeouts or payment failures (x402) may prevent results from being returned"],"whenToPreferThis":"Choose this endpoint when you have a fixed, enumerable set of candidate strings and need to deterministically rank them against a query, intent, or current state — especially in agentic control-flow scenarios where you need reproducible, explainable ordering rather than generative output. Prefer this over embedding-based vector search when the candidate set is small and known in advance, and over LLM-based ranking when determinism and cost predictability matter.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-09-14T06:36:28.513Z","isFirstParty":false}