crc_lnm_get_model_info

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

Annotations already declare readOnlyHint and idempotentHint, so the safety profile is covered. The description adds 'integrity-checked', implying the returned metadata is validated, but it does not explain what this check entails or describe any error behavior. This is minimal added context beyond annotations.

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 a single, front-loaded sentence stating the tool's purpose without redundancy. Every word contributes to the meaning, making it concise and well-structured.

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?

This is a simple read-only getter and an output schema exists to describe return values. However, the description omits any context about the required parameters and does not explain why integrity-checking matters or how the returned information fits into the broader workflow (e.g., before making predictions). The absence of usage guidance also affects completeness.

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?

The schema has 4 required parameters with 0% description coverage. The tool description does not mention contract_version, request_id, trace_id, or the empty input requirement, so it fails to compensate for the lack of parameter explanations. An agent would be left without any guidance on what these parameters mean or how to set them.

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 uses the specific verb 'Return' and identifies the exact content: model dimensions, version, hashes and threshold. It clearly distinguishes this tool from siblings like prediction or feature preparation by focusing on metadata retrieval.

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?

No guidance is given on when to use this tool versus alternatives like crc_lnm_predict_multimodal or crc_lnm_generate_report. The description does not mention any prerequisites, exclusions, or typical workflow placement.

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