{"uid":"cap_HtDiVJiFJqNMiC1_FQ_pa","slug":"signalharness-rag-retrieval-recall-k-evaluator-57d095c5","name":"SignalHarness RAG Retrieval Recall@K Evaluator","description":"Explore 330 pay-per-call x402 API services and 27 agent-native digital products, with Base USDC pricing, secure Polar checkout, and free discovery.","url":"https://signalharness.ai/api/agent/services/rag_retrieval_recall_at_k/invoke","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"request_json":{"type":"string","maxLength":65536,"minLength":2}}},"responseSchema":{"type":"json","example":{"replay":false,"result":{"warnings":["Verify the caller-supplied data before relying on this result."],"service_id":"rag_retrieval_recall_at_k","analysis_json":"{\"example\":\"schema-valid caller-supplied data\"}","evidence_scope":"caller_supplied_data"},"status":"succeeded","receipt":{"asset":"0x833589fCD6eDb6E08f4c7C32D4f71b54bdA02913","usage":[],"status":"succeeded","network":"eip155:8453","artifacts":[],"endedAtMs":0,"latencyMs":0,"paymentId":"example-payment","receiptId":"example-receipt","requestId":"example-request","serviceId":"rag_retrieval_recall_at_k","executionId":"example-execution","startedAtMs":0,"amountAtomic":"10000","resultSha256":"397ceca63c7c4d52dbad9b91f93f3e73659a0eb9e78aea7643719371629c1667","serviceVersion":"1.0.0","settlementReference":"0x0000000000000000000000000000000000000000000000000000000000000000"},"artifacts":[],"requestId":"example-request","executionId":"example-execution"}},"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_71z-hro1qP_QXJJY-z9qL","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":"Evaluates retrieval quality of a RAG (Retrieval-Augmented Generation) pipeline by computing Recall@K metrics from caller-supplied query and retrieved document data.","exampleAgentPrompt":"Evaluate the recall@K performance of my RAG retrieval results — here's the JSON with my queries, the ground-truth relevant documents, and the top-10 retrieved candidates per query; tell me what the recall score looks like.","exampleUseCases":[{"title":"Benchmarking a new vector search index","prompt":"I just swapped out my embedding model and want to know if retrieval got better or worse — can you compute recall@10 for these retrieved document sets against the ground truth I'm providing?"},{"title":"Debugging low answer quality in a RAG chatbot","prompt":"My RAG chatbot keeps giving wrong answers and I suspect the retrieval step is missing relevant docs — run a recall@5 evaluation on this sample of queries and retrieved chunks so I can see where it's falling short."},{"title":"Comparing retrieval strategies before deployment","prompt":"I have two sets of retrieval results from different chunking strategies and I need to know which one has better recall@20 — here's the JSON with both sets of candidates and the ground truth labels."}],"resultDescription":"Returns a JSON payload containing the computed Recall@K analysis in `analysis_json`, a service status of 'succeeded', any data quality warnings (e.g. to verify caller-supplied data), and a full receipt with payment ID, request ID, execution ID, latency, and a SHA-256 result hash for auditability.","failureModes":["Malformed or invalid `request_json` input returns a validation error","Missing ground truth or retrieved document data leads to incomplete analysis","Caller-supplied data quality warnings included in response when inputs cannot be fully verified","Payment failure on the x402 protocol may prevent execution","Empty or minimal input JSON may result in trivial or example-schema output"],"whenToPreferThis":"Choose this endpoint when you need a fast, pay-per-call, agent-native Recall@K metric for RAG or information retrieval pipelines without standing up your own evaluation infrastructure. It is ideal for agents that need to audit retrieval quality programmatically, especially in Base USDC micropayment workflows via x402.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-09-15T18:50:44.676Z","isFirstParty":false}