Tanod mlpeek Zero-Shot Text Classifier

x402

Tanod mlpeek Zero-Shot Text Classifier is a paid API for AI agents from tanod.dev, paid per call via x402, $0.001/call, status unknown (last checked 2026-10-09).

Classifies English text into 1–10 caller-supplied labels using NLI-based zero-shot classification (DeBERTa), returning confidence scores per label.

Price / call$0.001

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 array of labels each with a model confidence score (0–1), the model ID and license, token usage (label count and token count), whether the input was truncated, the multi_label flag, and the hypothesis template used. Scores sum to ~1 in single-label mode; each is independent in multi-label mode.

Example use cases

Customer support ticket routing

“I have a support message: 'My app keeps crashing whenever I try to upload a photo.' Classify it into one of these three labels: bug report, feature request, billing issue — pick the single best fit and tell me the confidence score.”

News article topic tagging

“Can you zero-shot classify this headline — 'Fed raises interest rates for the third time this quarter' — into one of these categories: politics, economics, sports, technology, entertainment? No training data, just use NLI.”

Multi-label content moderation

“Score this user comment — 'This product is absolute garbage and the seller is a scammer' — against all of these labels independently: hate speech, spam, product complaint, misinformation. Turn on multi-label mode so each label gets its own score.”

Technical details

Endpoint

POSThttps://tanod.dev/v1/classify/zero-shot?utm_source=zero.xyz

When to prefer

Choose this endpoint when you need fast, training-free text classification into a small set of custom labels without any labelled training data. Ideal for routing, tagging, or intent detection tasks where labels change frequently or per-request. Prefer over fine-tuned classifiers when no labelled data is available and over LLM-based classification when cost per call must be predictable at $0.001. Not suitable for non-English text or when calibrated probabilities (rather than model confidences) are required.

Side effects

Text exceeds 2000 characters — likely truncated or rejected
Fewer than 1 or more than 10 labels provided — validation error
hypothesis_template missing '{}' placeholder — bad request error
Non-English input — model may return low-quality or unreliable scores
All labels very similar — scores may be near-uniform with low discrimination
Payment not attached or insufficient — 402 Payment Required

Input

Schema · Request body

Field	Type	Description
text	string	English text, at most 2,000 characters.
labels	array	1-10 unique candidate labels, each at most 100 characters.
multi_label	boolean	Score each label on its own instead of making them compete.
hypothesis_template	string	Must contain {} exactly once.

Output

Schema · Response

{
  "type": "json",
  "example": {
    "note": "English NLI model; scores are model confidences, not calibrated probabilities",
    "model": {
      "id": "nli-deberta-v3-xsmall",
      "source": "cross-encoder/nli-deberta-v3-xsmall",
      "license": "Apache-2.0",
      "revision": "a150876415327c80daeff35ca6f68f5ed8cf5c24"
    },
    "usage": {
      "labels": 3,
      "tokens": 58
    },
    "labels": [
      {
        "label": "billing",
        "score": 0.941569
      },
      {
        "label": "sales",
        "score": 0.054991
      },
      {
        "label": "technical support",
        "score": 0.00344
      }
    ],
    "truncated": false,
    "multi_label": false,
    "hypothesis_template": "This example is about {}."
  }
}

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