tokscale_breakdown

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

Annotations already declare readOnlyHint=true, and the description adds valuable behavioral details: threshold logic for lumping into 'other', default of 1%, and a detailed return format. No contradictions with 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 extremely concise: two sentences plus a return format snippet. Every sentence is necessary and front-loaded with the core purpose. No waste.

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 low complexity (1 parameter, no required fields, output schema described inline), the description provides adequate context: what data is returned (platform, model, token counts, percentage) and the threshold behavior. The return format compensates for the lack of a formal output schema. Slightly loses a point for not explaining what 'platforms detected by tokscale' means, but that is minor.

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 coverage is 100% for the single parameter 'threshold'. The description implicitly references the parameter ('Models under the threshold...') but adds no new semantic information beyond what the schema already provides (default, type, description). Baseline of 3 is appropriate.

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's purpose: showing a per-model breakdown of token usage, with a specific verb 'Show' and resource 'per-model breakdown'. It distinguishes itself from sibling tools by focusing on model-level granularity per platform, and includes a concrete example (e.g., 'claude-opus-4-8 76%').

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 provides clear context for when to use the tool: 'Useful for seeing which models you actually use per platform.' It implies a specific use case but does not explicitly mention when not to use it or suggest alternative tools among the siblings.

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