llm_team_report

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

Without annotations, the description carries full behavioral transparency burden. It mentions 'displays' implying a read operation, but does not explicitly state it is read-only, does not mention authentication needs, side effects, or that it auto-detects user/project (though noted). It is adequate but not thorough.

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 with two short paragraphs: first sentence states purpose, second details what is displayed and the auto-detection logic, and then a clear argument documentation. Every sentence earns its place.

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 optional parameter) and presence of an output schema (so return values need not be described), the description is complete. It covers purpose, displayed content, auto-detection, and parameter options.

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?

The input schema has 0% description coverage, so the description compensates by listing all allowed values for the `period` parameter (today, week, month, all). It adds meaning beyond the schema, though it could be enhanced by explaining each period's scope.

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 shows a team savings report for the current user and project, listing specific metrics (call counts, cost savings, etc.). However, it does not distinguish from similar siblings like `llm_savings` or `llm_usage` which might also display savings data.

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?

No guidance is provided on when to use this tool versus alternatives like `llm_savings`, `llm_check_usage`, or `llm_usage`. The description only explains what it does, without any when-not or contextual recommendations.

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