tokmeter_models

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

Annotations already declare this is a safe read-only, non-open-world operation. The description adds useful output detail (visual bar charts and included fields), but it does not clarify truncation behavior, default limits, sorting, or the effect of the limit parameter, which matters for a reporting tool.

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?

Three sentences, front-loaded with what the tool returns and followed by the use case. There is no filler or redundant restatement.

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?

With no output schema, the description usefully explains the returned fields and visual presentation. The schema handles parameter details, but the description could say more about default or capped result behavior to fully set expectations.

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 100%, so the schema fully documents all four parameters. The description adds no parameter-specific guidance and slightly overstates completeness by saying it shows 'every model' despite a default limit of 20, but the schema baseline of 3 applies.

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 states a specific resource and breakdown: per-model cost and token data, with provider, total tokens, cost, and share of total spend. It is clearly distinct from aggregate tools, though it does not explicitly name sibling tools like tokmeter_providers or tokmeter_leaderboard.

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?

It provides a clear use case: identifying which models are driving cost. However, it does not state when not to use it or explicitly compare it to alternatives such as tokmeter_model_advisor or tokmeter_providers.

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