{"uid":"cap_Ih_DLOrW8UKWlRysCzO_3","slug":"tanod-mlpeek-rerank-0d337795","name":"Tanod mlpeek Rerank","description":"One MCP server and HTTP API with 193 pay-per-call tools for AI agents (security checks, chain data, finance, aviation, documents), plus free uptime and MikroTik monitoring and 130+ free browser tools. No signup; free daily tier. Operated by an AI.","url":"https://tanod.dev/v1/rerank?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"query":{"type":"string","maxLength":2000,"minLength":1,"description":"The query (at most 2,000 characters)."},"top_k":{"anyOf":[{"type":"integer","maximum":100,"minimum":1},{"type":"null"}],"default":null,"description":"Keep the best k (default: all, ranked)."},"documents":{"type":"array","items":{"type":"string","maxLength":4000,"minLength":1},"maxItems":100,"minItems":1,"description":"1-100 documents, each at most 4,000 characters."}}},"responseSchema":{"type":"json","example":{"note":"score = sigmoid(logit), a relevance in (0, 1); English model. Pairs longer than the window are truncated, longest side first (truncated: true).","model":{"id":"ms-marco-MiniLM-L6-v2","source":"cross-encoder/ms-marco-MiniLM-L6-v2","license":"Apache-2.0","revision":"233902d25c440f23af6f7d6e94d2946bac0bee0a"},"usage":{"tokens":33,"documents":2,"truncated":0},"results":[{"rank":1,"index":0,"logit":8.7,"score":0.999833,"tokens":17,"truncated":false},{"rank":2,"index":1,"logit":-11.3182,"score":0.000012,"tokens":16,"truncated":false}],"max_tokens_per_pair":512}},"example":null,"exampleRequest":null,"tags":["x402"],"displayCostAmount":"0.002","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":"$0.002/call","primary":{"kind":"static","protocol":"x402","network":"base","amountUsd":"0.002","per":"call","confidence":"exact"},"accepted":[{"kind":"static","protocol":"x402","network":"base","amountUsd":"0.002","per":"call","confidence":"exact"}]},"paymentMethods":[{"uid":"pm_b7KB2kPH557XWNf0DWImX","protocol":"x402","methodType":"crypto","chain":"base","mode":"charge","costAmount":"0.002","costPer":"request","priority":0,"asset":"0x833589fCD6eDb6E08f4c7C32D4f71b54bdA02913","unit":"request","depositMicros":null,"planRef":null}],"brandName":null,"brandSlug":null,"brandBaseUrl":null,"brandDocsUrl":null,"whatItDoes":"Scores and ranks up to 100 documents against a query using a cross-encoder model (ms-marco-MiniLM-L6-v2), returning relevance scores in (0,1).","exampleAgentPrompt":"I have a user question — 'What are the side effects of ibuprofen?' — and 10 candidate passages retrieved from my knowledge base. Rerank them by relevance to that question and give me just the top 3.","exampleUseCases":[{"title":"RAG pipeline result reranking","prompt":"I retrieved 20 chunks from my document store for the query 'how do I configure OAuth2 in FastAPI' — rerank all of them by relevance and return the top 5 so I can pass them to the LLM."},{"title":"FAQ answer selection","prompt":"A user asked 'how do I cancel my subscription?' and I have 15 FAQ entries as candidates. Score each one against that question and tell me which FAQ entry is the most relevant."},{"title":"Candidate document filtering","prompt":"I've got 50 product descriptions and a customer search query 'lightweight waterproof hiking jacket for cold weather' — rank all 50 by relevance score and show me the top 10."}],"resultDescription":"A JSON object containing: a ranked list of results each with rank position, original document index, logit score, sigmoid relevance score (0-1), token count, and truncation flag; plus aggregate usage stats (total tokens, document count, truncated count), model metadata (ID, source, license, revision), and a note about scoring and truncation behavior.","failureModes":["More than 100 documents submitted — request rejected","Document exceeds 4,000 character limit — truncated or rejected","Query exceeds 2,000 character limit — validation error","top_k value outside 1-100 range — validation error","Non-English text may yield lower-quality scores (model is English-optimized)","Very long query-document pairs truncated at 512 tokens, longest side first (truncated flag set to true)","Payment not included or insufficient — 402 response"],"whenToPreferThis":"Choose this endpoint when you need lightweight, fast cross-encoder reranking of up to 100 documents without deploying your own model. Ideal for RAG pipelines where a bi-encoder retriever returns candidates that need precise relevance rescoring. Prefer over embedding-based similarity when ranking quality matters more than speed. The ms-marco-MiniLM-L6-v2 model is well-suited for English passage ranking tasks. At $0.002 per call it is cost-effective for on-demand per-query reranking in agent workflows.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-13T00:30:04.879Z","isFirstParty":false,"canonicalSlug":"tanod-mlpeek-rerank-0d337795"}