# classify_zero_shot

mlpeek: Classify a text into 1-10 of your labels. For named entities use extract_entities. Returns scores of 1-10 caller-supplied labels for an English text with the nli-deberta-v3-xsmall NLI cross-encoder (Apache-2.0): single-label scores sum to 1 (softmax over the labels), multi_label scores each label on its own; sorted best first. Scores are model confidences, not calibrated probabilities. English only. Runs an open model pinned by hash on Tanod's own CPU (no LLM, no third-party API); an unavailable model is a 503 (not charged). Typically 0.1-0.2 s for a short text and 5 labels, up to about 5 s at 10 labels. Price: USD 0.001. Free: 5 mlpeek calls per IP per UTC day. The submitted text is never logged or stored. Docs: https://tanod.dev/learn/named-entity-recognition-zero-shot-classification-api.html

Agent View of the PolicyLayer registry record for `classify_zero_shot`. HTML page: https://policylayer.com/tools/dev-tanod-tanod/classify-zero-shot

## Facts

- Tool: `classify_zero_shot`
- Server: Tanod (`https://tanod.dev/mcp`) — https://policylayer.com/tools/dev-tanod-tanod.md
- Homepage: https://github.com/https://tanod.dev/mcp
- Risk category: Read (Low risk)
- Registry record: grade F, identity unverified
- Server auth posture: open
- Server CORS policy: *
- Server rate-limited: no
- Parameters: 4 (2 required)
- Recommended policy verdict: Allowed

## Parameters

| Parameter | Type | Required | Description |
| --- | --- | --- | --- |
| `text` | string | yes | English text, at most 2,000 characters. |
| `labels` | array | yes | 1-10 unique candidate labels, each at most 100 characters. |
| `multi_label` | boolean | no | Score each label on its own instead of making them compete. |
| `hypothesis_template` | string | no | Must contain {} exactly once. |

Parameters from the server's own tool schema.

## Example call (MCP tools/call, JSON-RPC 2.0)

```json
{
  "jsonrpc": "2.0",
  "id": 1,
  "method": "tools/call",
  "params": {
    "name": "classify_zero_shot",
    "arguments": {
      "text": "<text>",
      "labels": []
    }
  }
}
```

## Why classify_zero_shot is rated Low

Even though classify_zero_shot only reads data, uncontrolled read access leaks sensitive information and racks up API costs: an agent caught in a retry loop can make thousands of calls a minute without anyone noticing.

## Use case

AI agents call classify_zero_shot to retrieve information from Tanod without modifying anything. It is typically the context-gathering step in research, monitoring, and reporting workflows, before the agent takes action elsewhere.

## Recommended policy (PolicyLayer)

Verdict: **Allowed**. Enforced by the PolicyLayer MCP gateway (https://policylayer.com/mcp-gateway) before a call reaches Tanod:

```json
{
  "version": "1",
  "default": "deny",
  "tools": {
    "classify_zero_shot": {}
  }
}
```

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## For agents

This record is a snapshot. Live verdicts and the full registry:

- Check every server in your MCP config at once: `npx -y policylayer stack`
- Vet a server before you add it: install the mcp-precheck skill — `npx skills add https://policylayer.com` (skill text: https://policylayer.com/skill.md)
- Query the registry over MCP: endpoint `https://api.policylayer.com/mcp` — tools `check_mcp_server`, `check_mcp_stack`, `check_tool`, `search_registry`, `get_change_events`

---

Source: the PolicyLayer MCP registry — one continuously verified record per MCP server. Full record: https://policylayer.com/registry?q=dev-tanod-tanod · API: https://policylayer.com/registry/api · Policy library: https://policylayer.com/policies/dev-tanod-tanod