hf_inference.nlp.ner
Extract named entities — people (PER), locations (LOC), organizations (ORG), and miscellaneous (MISC) — from text using a BERT-based NER model via HuggingFace Inference API. Returns each detected entity with its type, confidence score, and character positions in the original text. Default model: ...
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What hf_inference.nlp.ner does on Apibase
AI agents call hf_inference.nlp.ner to retrieve information from Apibase 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 | Input text to process. Maximum ~10,000 characters depending on model context window. |
model | string | — | HuggingFace model ID to use for named entity recognition. Default: "dbmdz/bert-large-cased-finetuned-conll03-english" (English NER: PER, LOC, ORG, MISC). Altern |
Parameters from the server's own tool schema.
Why hf_inference.nlp.ner is rated Low
Tool analyzes text and returns entity classifications without modifying data or triggering external actions.
From the tool's definition Extract named entities from text using BERT-based NER model via HuggingFace Inference API.
Attacks that exploit this kind of access
The rule that runs hf_inference.nlp.ner safely
PolicyLayer is an MCP gateway: it sits between your AI agents and Apibase, and checks every tool call against a rule you set before the call runs. Nothing changes on the server itself. For hf_inference.nlp.ner, this is the rule to start with:
hf_inference.nlp.ner 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 Apibase, apply this rule, and every hf_inference.nlp.ner call is checked against it from then on.
Questions about hf_inference.nlp.ner
Extract named entities — people (PER), locations (LOC), organizations (ORG), and miscellaneous (MISC) — from text using a BERT-based NER model via HuggingFace Inference API. Returns each detected entity with its type, confidence score, and character positions in the original text. Default model: dbmdz/bert-large-cased-finetuned-conll03-english (CoNLL-2003, English). Useful for document parsing, contact extraction, and knowledge graph construction. It is categorised as a Read tool in the Apibase MCP Server, which means it retrieves data without modifying state.
hf_inference.nlp.ner accepts 2 parameters: text, model. Required: text. The full parameter table on this page comes from the server's own tool schema.
Register the Apibase MCP server in PolicyLayer and add a rule for hf_inference.nlp.ner: 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 Apibase. Nothing to install.
hf_inference.nlp.ner 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 hf_inference.nlp.ner 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 hf_inference.nlp.ner. 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.
hf_inference.nlp.ner is provided by the Apibase MCP server (apibase-mcp-client). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
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