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

Annotations already declare readOnly, idempotent, non-destructive behavior, so the description does not need to repeat safety traits. It adds meaningful behavioral context beyond annotations: the tool analyzes a question's source-pool occupation, classifies platforms, detects brand/domain presence, and returns competitor domains and suggested actions. No annotation contradiction exists.

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 well-organized into purpose, rationale, return contents, and parameter semantics. Every section earns its place: the principle explains why the tool matters, the return list sets expectations, and the parameter notes use concrete examples. The first sentence front-loads the core purpose.

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 3-parameter read-only probe tool with an output schema, the description is complete: it covers what the tool does, why it exists, what it returns, and how to fill each parameter. The output schema handles structural details, so the description does not need to enumerate return types further.

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 description coverage at 0%, the description carries the full burden of explaining parameters. It explicitly defines question as the user's real wording, brand as the brand term with an example, and domain as the user's domain with an example. This converts opaque parameter names into actionable instructions.

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 states a specific action—probe the actual source-pool distribution for a Chinese question—and the resource (信源池), plus the secondary judgment of whether the brand/domain appears. This clearly differentiates it from the sibling tools (audit_cn_citability, plan_fixes, score_visibility) which sound like audit/fix/scoring utilities.

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 gives clear context: use this when you need to know whether a brand/domain has already entered the retrieval pool for a real question, and explains the underlying reason (AI answers only cite sources already in the pool). It does not explicitly name alternatives or when-not-to-use conditions, but the purpose context is strong enough to guide selection.

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