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groundlens_dgi

Read-onlyIdempotent

Detect hallucination in LLM responses by measuring geometric alignment between question and answer without needing source context.

Instructions

Check whether an LLM response shows hallucination patterns (DGI).

DGI (Directional Grounding Index) measures whether the question-to-response displacement aligns with the direction characteristic of verified grounded responses. No source context is needed — this works for open-ended chat, general Q&A, or any situation where you just have a question and answer.

A positive score means the displacement aligns with grounded patterns. A score below 0.30 means the response is geometrically anomalous. A negative score means high hallucination risk.

Args: params (DGIInput): The question and LLM response.

Returns: str: JSON with a plain-language CHECK, the DGI score, and the magnitude.

Examples: - Checking a chatbot's answer to a factual question - Screening LLM outputs before showing them to users - Batch-evaluating model responses for quality

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
paramsYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

The description goes beyond annotations by detailing the DGI score interpretation, return format (JSON with CHECK, score, magnitude), and behavioral notes like 'positive score means grounded patterns'. No contradiction with readOnlyHint or idempotentHint.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is well-structured with sections for metric explanation, score interpretation, args, returns, and examples. It is reasonably concise but could be slightly shortened without losing clarity.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The description fully explains the tool's purpose, metric interpretation, and return format. It provides enough context for an AI agent to decide when to use it (no source context needed) and what output to expect.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema already has descriptions for 'question' and 'response' fields, so the description adds minimal extra semantic value beyond stating they are used for DGI calculation. Schema coverage is high, so baseline 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool checks for hallucination patterns using DGI, explains the metric, and notes it works without source context. It subtly differentiates from siblings by emphasizing context-free operation.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description lists explicit use cases: open-ended chat, general Q&A, and batch evaluation. It implies when to use by stating 'no source context needed', but does not explicitly exclude scenarios or compare with siblings.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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