llm_team_report

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

No annotations are provided, so the description carries the full burden of disclosure. It does not explicitly state whether the tool is read-only, modifies state, or requires authentication. While it describes what the report shows, it omits important behavioral traits like whether it performs any side effects.

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 structured with a one-sentence summary followed by a detailed breakdown and an Args section. It is not overly long, but the Args section could be more terse. The front-loaded summary is effective.

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 presence of an output schema, the description need not explain return values. It adequately describes the components of the report. However, it does not mention the output schema or confirm that no further documentation is needed. It is sufficiently complete for a simple reporting tool.

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?

With 0% schema description coverage, the description compensates by explaining the permissible values for the 'period' parameter ('today', 'week', 'month', 'all'). This adds significant meaning beyond the schema, which only provides a default. The parameter is well-documented in the text.

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 it shows a team savings report with specific breakdowns (call counts, cost savings, etc.) for the auto-detected user and project. This is a specific verb+resource combination that distinguishes it from siblings like llm_savings or llm_dashboard.

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 auto-detection mechanism and the period argument, but provides no guidance on when to use this tool versus alternatives (e.g., when to use llm_team_report vs llm_savings vs llm_usage). Usage context is implied but not explicit.

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