ai_usage_log_stats

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

Without annotations, the description carries full burden. It correctly indicates a read-only operation (stats), but doesn't disclose potential behaviors like required permissions, rate limits, or data refresh semantics. The derived metrics detail adds some transparency, but safety aspects are missing.

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?

A single, well-structured sentence that front-loads the key value ('Dashboard KPIs') and lists specific outputs. Every word earns its place—no fluff, high information density.

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 covers return values by listing metric categories. However, it could specify the time window for '30-day timeseries' more precisely and mention if totals are aggregated across all time. Still, it's largely complete for its purpose.

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 only 33% (period has description). The description does not add meaning to 'connection' or 'tenant_code' parameters. The overall tool description helps but doesn't compensate for the low per-parameter documentation.

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 'Dashboard KPIs for the billing ledger' and enumerates specific metrics (totals, per-model breakdown, timeseries, errors, derived metrics), making the purpose explicit and distinguishing it from sibling tools like ai_usage_log_query.

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 implies usage for aggregate stats, but lacks explicit guidance on when to use versus alternatives like ai_usage_budget_status or ai_usage_log_query. However, the mention of 'Dashboard KPIs' provides clear context for data exploration.

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