Tanod mlpeek Rerank

x402

Tanod mlpeek Rerank is a paid API for AI agents from tanod.dev, paid per call via x402, $0.002/call, status unknown (last checked 2026-10-09).

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).

Price / call$0.002

Seller volume →—

Success—

StatusUnknown

How this service works

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.

Output

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.

Example use cases

RAG pipeline result reranking

“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.”

FAQ answer selection

“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.”

Candidate document filtering

“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.”

Technical details

Endpoint

POSThttps://tanod.dev/v1/rerank?utm_source=zero.xyz

When to prefer

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.

Side effects

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

Input

Schema · Request body

Field	Type	Description
query	string	The query (at most 2,000 characters).
top_k	—	Keep the best k (default: all, ranked).
documents	array	1-100 documents, each at most 4,000 characters.

Output

Schema · Response

{
  "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
  }
}

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