get_usage_stats

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

With no annotations provided, the description carries the full burden of transparency. It describes the returned metrics and period comparisons, but does not disclose any behavioral traits such as data freshness, permission requirements, or rate limits. It is adequate but minimal.

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 two sentences long, front-loaded with the core purpose, and contains no filler. Every word adds value, making it an example of concise and well-structured documentation.

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?

The description lists the metrics returned but lacks details on the output structure or example. With no output schema, a bit more detail would improve completeness. However, for a simple statistics tool, the current level is average.

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 covers all 5 parameters with descriptions, achieving 100% coverage. The description adds no additional meaning beyond the schema, only repeating the date range implicitly. A baseline score of 3 is appropriate since the schema already provides sufficient 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 returns call usage statistics including total calls, pickup rate, duration, costs, and unique users. It also specifies that metrics include current period, previous period, and percent change, making the purpose unambiguous and distinct from related tools like get_call_counts_by_day or get_pickup_rates.

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 says 'Useful for understanding usage patterns and costs,' which implies when to use it but does not explicitly state when not to use it or how it differs from alternatives like get_usage_breakdown or get_daily_summary. No exclusions or conditions are provided.

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