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

No annotations are provided. The description does not disclose whether the tool is read-only, destructive, requires authentication, or has rate limits. It only states the input and outputs, leaving behavioral gaps for an AI agent.

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 concise sentence that captures the core functionality without any extraneous content. Every word contributes meaning.

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?

The description lists the metrics returned, which is helpful given no output schema. However, it fails to mention the model parameter or differentiate from sibling tools like stabilize_for_cache. The completeness is adequate but not comprehensive.

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%. The description does not explain the purpose of any parameters (original, distilled, model). The term 'uncompressed and distilled text formats' loosely maps to original and distilled, but model is entirely unaddressed.

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 the tool generates comparison metrics (token count reduction, compression ratio, cost savings) between uncompressed and distilled text formats. It uses specific verbs and resources, and aligns with sibling tools that focus on distillation processes.

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 implies usage when having uncompressed and distilled texts to compare, but provides no explicit guidance on when to use this tool versus alternatives like analyze_tokens or the distill_* tools. No when-not or prerequisuties are mentioned.

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