hf_inference.nlp.summarize
Summarize a long text into a shorter, coherent paragraph using the facebook/bart-large-cnn model via HuggingFace Inference API. Trained on CNN/DailyMail news articles; works well for factual prose. Control output length with max_length (token cap) and min_length (token floor) parameters. Custom m...
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What hf_inference.nlp.summarize does on Apibase
AI agents call hf_inference.nlp.summarize 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 summarization. Default: "facebook/bart-large-cnn" (trained on CNN/DailyMail, excellent for news and articles). Alternatives: "ss |
max_length | integer | — | Maximum number of tokens in the generated summary (20–1024). Default: model-controlled (typically ~150 tokens for BART-large-CNN). Set lower for shorter summari |
min_length | integer | — | Minimum number of tokens in the generated summary (10–512). Prevents very short or empty summaries. Default: model-controlled (typically ~30 tokens). Set min_le |
Parameters from the server's own tool schema.
Why hf_inference.nlp.summarize is rated Low
Non-destructive text summarization via API with no side effects or data modification.
From the tool's definition Summarize a long text into a shorter, coherent paragraph using the facebook/bart-large-cnn model
Attacks that exploit this kind of access
The rule that runs hf_inference.nlp.summarize 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.summarize, this is the rule to start with:
hf_inference.nlp.summarize 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.summarize call is checked against it from then on.
Questions about hf_inference.nlp.summarize
Summarize a long text into a shorter, coherent paragraph using the facebook/bart-large-cnn model via HuggingFace Inference API. Trained on CNN/DailyMail news articles; works well for factual prose. Control output length with max_length (token cap) and min_length (token floor) parameters. Custom model override supported (e.g. google/pegasus-xsum for extreme single-sentence summaries). Useful for article digests, executive summaries, and reducing LLM context window usage. It is categorised as a Read tool in the Apibase MCP Server, which means it retrieves data without modifying state.
hf_inference.nlp.summarize accepts 4 parameters: text, model, max_length, min_length. 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.summarize: 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.summarize 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.summarize 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.summarize. 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.summarize 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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