tokmeter_digest

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

Annotations already declare readOnlyHint=true and openWorldHint=false, so safety is covered. The description adds useful behavioral context: the output is a prose briefing covering key stats, top spenders, trends, and actionable insights. It does not describe limitations or data source caveats.

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 three sentences and front-loads the core purpose. There is minor redundancy between 'concise natural language summary' and 'in prose form,' but no sentence is wasted.

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 appropriately explains the return shape as a prose briefing with stats, top spenders, trends, and insights. All three parameters are fully documented in the schema, and annotations cover the safety profile, leaving only minor unstated details.

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 scope, project, and providers. The description does not add syntax, defaults, or interpretation beyond what the schema provides, so the 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 states a specific verb and resource: generating a natural language summary of token usage. It clarifies the output format as prose rather than tables, but it does not explicitly differentiate against named sibling tools like tokmeter_pulse or tokmeter_timeline.

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 gives a clear usage context: use when the user wants a quick narrative overview instead of tables. However, it does not name specific alternative tools or state when not to use this tool beyond the general 'rather than tables' contrast.

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