usage summary (Token Meter)

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

Annotations already declare readOnlyHint=true, and the description adds that the data is an 'API-equivalent estimate, local data only' and provides a table with advisory narrative. This adds useful behavioral context without contradicting 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 dense paragraph that could be broken into cleaner sections. While not overly long, it uses parentheticals and dashes that reduce readability. Concise but could be better 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?

Given no output schema, the description adequately outlines the output as a 'table by day × models' plus narrative. It covers key parameters and their defaults. Missing explicit details about the exact shape or granularity of the output, but sufficient for an agent to understand what to expect.

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 low (33%), but the description explains `scope` filtering and `insights` toggle. The `period` parameter is self-explanatory via enum values. The description offsets the schema gaps by clarifying the non-obvious behaviors of scope and insights.

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 provides a summary of token usage ('What you spent, where it went, and what was slow') for Claude Code and Codex. It implicitly differentiates from siblings by focusing on aggregated data, but explicit comparison to `recent_sessions` or `refresh_data` is missing.

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 explains the scope parameter's default behavior and the effect of 'all' vs 'auto', and mentions insights toggle, but does not advise when to prefer this tool over siblings or when not to use it. Contextual usage guidance is present but not comprehensive.

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