groundlens_sgi

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

Annotations already indicate readOnlyHint=true, idempotentHint=true, destructiveHint=false, and the description adds behavioral details: explains the SGI ratio formula, what high/low scores mean, and the return format (JSON with CHECK, score, distances). This goes well beyond annotations, providing full transparency.

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

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

The description is well-structured: first sentence states purpose, then explains the metric, then Args/Returns/Examples. It is concise with no wasted words, fully front-loaded, and every sentence adds value.

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

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

Given the tool's complexity (metric calculation), annotations, and schema, the description is complete. It covers when to use, how the score works, what parameters are needed, what the output looks like, and provides examples. The output schema is mentioned in context signals and the description describes the return format sufficiently.

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

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

Schema description coverage is 0% according to context signals, but the nested SGIInput type has descriptions for each field (question, context, response). The tool description repeats the Args without adding new semantic meaning beyond the schema. However, it does explain the functional role of the parameters in the context of the metric, which adds some value. Baseline 3 due to good schema descriptions.

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

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

The description clearly states it checks LLM response grounding in a source document using SGI, and distinguishes itself as the method for RAG pipeline verification. The specific verb 'Check' and resource 'grounding' are well-defined, and the context signals show sibling tools, implying differentiation.

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

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

The description explicitly says 'use for RAG pipeline verification' and provides examples of when to use it (verifying RAG chatbot, checking summary faithfulness, auditing context ignoring). It does not explicitly state when not to use or alternatives, but the examples and positioning give clear usage context.

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