hallucination_confidence_meter

Evaluates the likelihood of hallucination in LLM responses by comparing against HuggingFace model confidence scores. Designed for risk assessment personas to quantify response reliability. Accepts text snippets or model outputs, returns confidence metrics and potential hallucination warnings. Cro...

SERVERMcp Knowledge SOURCEhttps://mcp.gapup.io
Low RISK CLASS
Category Read
Parameters 41 required
Recommended Allowedsee the rule below
Registry record Grade F, identity unverified Pull the record →

This record as markdown: /tools/io-github-getgapup-mcp-knowledge/hallucination-confidence-meter.md

What hallucination_confidence_meter does on Mcp Knowledge

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

ParameterTypeRequiredDescription
text string Yes The LLM-generated text to evaluate for hallucination risk
async boolean If true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client ti
model_id string Optional specific HuggingFace model ID to use for evaluation
threshold number Confidence threshold below which hallucination warnings are triggered

Parameters from the server's own tool schema.

Why hallucination_confidence_meter is rated Low

This tool reads and analyzes text input, comparing it against external model confidence scores and leaderboard data. It produces a report/metrics output with no side effects—no data is written, deleted, or executed. It is purely a retrieval and analysis operation.

From the tool's definition Evaluates the likelihood of hallucination... returns confidence metrics and potential hallucination warnings. Cross-references with top-performing models from the HuggingFace leaderboard.

Questions about hallucination_confidence_meter

What does the hallucination_confidence_meter tool do? +

Evaluates the likelihood of hallucination in LLM responses by comparing against HuggingFace model confidence scores. Designed for risk assessment personas to quantify response reliability. Accepts text snippets or model outputs, returns confidence metrics and potential hallucination warnings. Cross-references with top-performing models from the HuggingFace leaderboard. It is categorised as a Read tool in the Mcp Knowledge MCP Server, which means it retrieves data without modifying state.

What parameters does hallucination_confidence_meter accept? +

hallucination_confidence_meter accepts 4 parameters: text, async, model_id, threshold. Required: text. The full parameter table on this page comes from the server's own tool schema.

How do I enforce a policy on hallucination_confidence_meter? +

Register the Mcp Knowledge MCP server in PolicyLayer and add a rule for hallucination_confidence_meter: 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 Mcp Knowledge. Nothing to install.

What risk level is hallucination_confidence_meter? +

hallucination_confidence_meter is a Read tool with low risk. Read-only tools are generally safe to allow by default.

Can I rate-limit hallucination_confidence_meter? +

Yes. Add a rate_limit block to the hallucination_confidence_meter 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.

How do I block hallucination_confidence_meter completely? +

Set action: deny in the PolicyLayer policy for hallucination_confidence_meter. 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.

What MCP server provides hallucination_confidence_meter? +

hallucination_confidence_meter is provided by the Mcp Knowledge MCP server (https://mcp.gapup.io). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.

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