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groundlens_dgi

Read-onlyIdempotent

Rank LLM responses by grounding quality when no source document is available. Provides a DGI score to prioritize reviews of open-ended chat or general Q&A outputs.

Instructions

Coarse context-free grounding signal, for when no source is available (DGI).

DGI (Directional Grounding Index) compares the question-to-response displacement against the direction typical of answers written from a source. No context is needed, so it works for open-ended chat and general Q&A.

IMPORTANT: this is the weakest signal here and it has a measured ceiling. With authorship held constant it reaches AUROC 0.606, and the ceiling of the entire embedding-similarity class is about 0.68. It is a ranking signal for triage, not a detector, and it is not a risk verdict. Prefer groundlens_sgi whenever a source is available. Never report a DGI score as evidence that an answer is true or false.

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

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

Examples: - Ranking a batch of chat answers so a reviewer starts with the worst - Screening outputs when no source document exists

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
paramsYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior4/5

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

Discloses limitations: 'weakest signal', measured AUROC ceiling 0.606, ranking signal, not detector. Adds behavioral context beyond annotations (readOnlyHint, 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?

Well-structured with sections, front-loaded summary, and each sentence adds value. Slightly verbose but efficient overall.

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?

Covers use cases, limitations, and return format. Output schema exists, so return details are optional. Given tool complexity, description is complete and appropriate.

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?

Description mentions 'params (DGIInput): The question and LLM response' but adds little beyond schema. Schema descriptions already cover parameters, so no major gap, but description doesn't compensate for low schema_description_coverage (0%).

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's purpose: 'coarse context-free grounding signal' for when no source is available, and distinguishes it from sibling tools by recommending groundlens_sgi when a source exists.

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

Usage Guidelines5/5

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

Explicitly states when to use (no source available), when not to use ('Never report as evidence'), and suggests alternative (groundlens_sgi).

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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