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

Annotations already declare readOnlyHint=true, idempotentHint=true, destructiveHint=false and openWorldHint=false, so the safety profile is covered. The description adds the domain context that prompts are periodically checked across models, but says nothing about pagination behavior or result ordering beyond what the params imply.

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 purpose leads, followed by a useful domain clarification and a compact arg list. Slightly longer than strictly needed, but every sentence contributes and nothing is buried.

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?

With no output schema, the description could say more about the returned shape (e.g., total counts for pagination), but it covers purpose, scope, required input and all key filters adequately for a read-only list tool on a 6-param schema.

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 coverage is only 17%, so the description carries the load: it documents brand_id as required, active_only, tag_ids as comma-separated, limit (1-500, default 100) and offset (default 0). Only user_intent is left to the schema, a minor omission given the otherwise strong compensation.

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+resource ('List tracked search queries (prompts)') scoped to a brand, and the follow-up sentence explains what a prompt is so an agent distinguishes it from sibling mutations like manage_prompt or suggest_prompts.

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 explains what prompts are and that they are monitored across AI models, which implies the tool's purpose, but it never states when to use this over alternatives such as suggest_prompts, bulk_manage_prompts, or manage_prompt, nor any exclusions.

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