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

Annotations already declare read-only, idempotent, and non-destructive behavior. The description adds valuable context about what data is returned (top connectors, tools, skills, question types) and the concept of 'value received', going beyond the schema. No contradiction with annotations.

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?

Two well-structured sentences: the first states the purpose and specific output contents, the second lists trigger phrases. Every sentence earns its place, no unnecessary length.

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?

For a simple tool with one optional documented parameter, strong safety annotations, and a thorough description of what it returns and when to use, the description is complete enough for an agent to select and invoke it correctly.

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 schema provides a complete description for the only parameter (top_n), explaining it controls how many top connectors/tools/skills to return. The tool description does not add any additional parameter details, so the baseline score of 3 is appropriate.

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 personalized user statistics and usage summary for the current user, enumerating specific metrics (total tool calls, skill invocations, single-source vs multi-source questions, top connectors/tools/skills). It distinguishes from the sibling 'get_user_statistics' by emphasizing 'current user' and 'personalized', though it doesn't explicitly name the sibling.

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?

Provides explicit trigger phrases and usage context ('when the user asks for user stats, usage statistics, what have I used?'). However, it does not mention when not to use this tool or recommend alternative tools for other scenarios, so it lacks exclusions.

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