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 ea...
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What classify_zero_shot does on Tanod
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.
| 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 | — | Score each label on its own instead of making them compete. |
hypothesis_template | string | — | Must contain {} exactly once. |
Parameters from the server's own tool schema.
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.
Attacks that exploit this kind of access
The rule that runs classify_zero_shot safely
PolicyLayer is an MCP gateway: it sits between your AI agents and Tanod, and checks every tool call against a rule you set before the call runs. Nothing changes on the server itself. For classify_zero_shot, this is the rule to start with:
classify_zero_shot is read-only, so it stays allowed. Everything else on the server is denied unless you say otherwise.
The button opens the PolicyLayer dashboard: create your workspace, connect Tanod, apply this rule, and every classify_zero_shot call is checked against it from then on.
Questions about 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. It is categorised as a Read tool in the Tanod MCP Server, which means it retrieves data without modifying state.
classify_zero_shot accepts 4 parameters: text, labels, multi_label, hypothesis_template. Required: text, labels. The full parameter table on this page comes from the server's own tool schema.
Register the Tanod MCP server in PolicyLayer and add a rule for classify_zero_shot: allow, deny, rate-limit, or require approval. Point your MCP client at the PolicyLayer proxy URL and the rule is enforced on every call, before it reaches Tanod. Nothing to install.
classify_zero_shot is a Read tool with low risk. Read-only tools are generally safe to allow by default.
Yes. Add a rate_limit block to the classify_zero_shot rule in your PolicyLayer policy. For example, setting max: 10 and window: 60 limits the tool to 10 calls per minute. Rate limits are tracked per agent session and reset automatically.
Set action: deny in the PolicyLayer policy for classify_zero_shot. The AI agent will receive a policy violation error and cannot call the tool. You can also include a reason field to explain why the tool is blocked.
classify_zero_shot is provided by the Tanod MCP server (https://tanod.dev/mcp). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
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