get_model_usage

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 behavioral disclosure. It describes what data is retrieved (usage breakdown per day) but lacks details on permissions required, rate limits, pagination, error handling, or the format of the returned data. For a read operation with zero annotation coverage, this leaves significant gaps in understanding how the tool behaves beyond its basic function.

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 front-loaded with the core purpose in the first sentence and adds value with a second sentence on utility, with no wasted words. It efficiently communicates essential information without redundancy, making it easy for an agent to parse and understand quickly.

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 moderate complexity (3 parameters, no output schema, no annotations), the description is adequate but incomplete. It covers the purpose and high-level use case but lacks details on behavioral traits (e.g., data format, limitations) that would help an agent invoke it correctly. Without annotations or output schema, more context on what to expect from the tool would improve completeness.

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%, with all three parameters (startDate, endDate, users) well-documented in the input schema, including formats and defaults. The description does not add any parameter-specific information beyond what the schema provides, such as explaining interactions between parameters or usage examples. Baseline score of 3 is appropriate as the schema handles the heavy lifting.

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 specific action ('Get model usage breakdown per day') and resource ('which models are being used'), distinguishing it from siblings like 'get_daily_usage' or 'get_mcp_usage' by focusing on model-level metrics. It explicitly mentions the key data points: models used, message counts, and user counts, making the purpose highly specific and differentiated.

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 context ('Essential for understanding model adoption and cost drivers'), suggesting it's for analytics and cost analysis, but does not explicitly state when to use this tool versus alternatives like 'get_daily_usage' or 'get_spending'. No exclusions or prerequisites are mentioned, leaving the agent to infer appropriate scenarios based on the purpose alone.

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