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

Annotations already cover the safety profile (readOnly, idempotent, non-destructive, closed-world), so the bar is lower, and the description still adds substantive behavior: when `period` is supplied, `days` is ignored, the response carries a `period` block, total_queries becomes measured days, visibility_rate becomes an average, and top_3 fields are null. That is meaningful disclosure about a mode switch beyond what annotations provide.

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?

Front-loads the purpose, then the platform scope, then a compact Args block. Every line carries information; the only minor drag is that the purpose sentence and the 'Useful for' sentence partly overlap.

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?

There is no output schema, so the description correctly explains the return-shape difference in `period` mode. It does not describe the normal (non-period) per-model return fields, which is a modest gap for an analytics tool, but nothing needed to invoke it correctly is missing.

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?

With schema coverage at only 20%, the description does the heavy lifting: it documents brand_id (required), days (7-365, default 30), period (YYYY-MM or YYYY-Qn format plus its override behavior), and include_trends. Only user_intent is left to the schema, and its schema text is self-explanatory, so the description nearly compensates for the coverage gap.

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?

States a specific verb and resource ('Get visibility breakdown by AI model') and clarifies the scope is per-platform (ChatGPT, Perplexity, Gemini, Claude, Copilot), which distinguishes it from the overall sibling get_visibility_scores. It does not explicitly name that sibling as the alternative, so it stops short of a 5.

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?

'Useful for understanding which AI models mention the brand most' implies a use case but never states when to pick this over get_visibility_scores, compare_brands, or compare_reports. No exclusions or preconditions are given, leaving the routing decision to inference.

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