cursor-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 states the tool retrieves data ('Get top file extensions'), implying a read-only operation, but doesn't mention potential side effects, authentication needs, rate limits, or data format. For a tool with zero annotation coverage, this leaves significant gaps in understanding its behavior.

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 a single, efficient sentence that front-loads the core purpose and includes key details (metrics: AI suggestions, accepts, rejects). There's no wasted verbiage or redundancy, making it easy to parse 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 partially complete. It clarifies the purpose and metrics but lacks behavioral context (e.g., data format, limitations) and usage guidelines. Without an output schema, it doesn't explain return values, leaving gaps in understanding what the tool delivers.

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%, so the schema already documents all three parameters (startDate, endDate, users) with formats and defaults. The description doesn't add any parameter-specific details beyond what's in the schema, such as explaining how 'users' filtering interacts with the metrics. Baseline 3 is appropriate when the schema handles 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 specific action ('Get top file extensions') and resource ('being edited with AI'), distinguishing it from sibling tools like get_agent_edits or get_model_usage by focusing on file extension analytics rather than agent edits or model usage. It specifies the exact metrics involved: AI suggestions, accepts, and rejects.

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 explicit guidance is provided on when to use this tool versus alternatives. While it implies usage for analyzing AI editing patterns by file type, it doesn't mention prerequisites, exclusions, or compare to siblings like get_usage_events or get_daily_usage that might offer related data. The description lacks context for tool selection.

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