get_visit_trends

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It describes the return format ('time series data points with visit counts and unique counts') and grouping behavior, but lacks details on permissions, rate limits, error handling, or data freshness. It adds some context but misses key operational traits.

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 appropriately sized and front-loaded in a single, efficient sentence that states the core functionality upfront. Every phrase adds value without redundancy, making it easy for an AI agent 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 complexity (7 parameters, no annotations, no output schema), the description is moderately complete. It covers the purpose and return format but lacks output details (e.g., data structure, pagination) and behavioral context (e.g., performance, limits). For a trend analysis tool with rich parameters, more completeness is needed.

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 7 parameters thoroughly. The description adds minimal value beyond the schema by mentioning 'optional grouping by subscription, account, or group' and 'daily, weekly, or monthly intervals', which are already covered in the schema. Baseline 3 is appropriate as the schema does 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 tool's purpose with specific verbs ('Get visit/usage count trends') and resources ('time series data points with visit counts and unique counts'). It distinguishes from siblings by focusing on temporal trends with grouping options, unlike summary or breakdown tools in the sibling list.

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 through 'trends over time' and 'optional grouping', but does not explicitly state when to use this tool versus alternatives like 'get_usage_summary' or 'get_platform_breakdown'. No exclusions or clear alternatives are provided, leaving usage guidance incomplete.

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