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

With no annotations, the description carries the full burden of behavioral disclosure. It thoroughly explains what an appearance means (and what it does not mean), counts one appearance per answer regardless of pages cited, uses the latest check per cell, and notes that answers with no captured evidence contribute nothing. This goes far beyond a simple tool description and gives the agent a clear mental model.

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 a single dense paragraph, but every sentence contributes meaningful constraints and disambiguation. The main purpose is front-loaded, and the subsequent clarifications are necessary to prevent misinterpretation. While it could be broken into bullet points for easier scanning, the length is justified by the complexity of the counting and semantics.

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 tool with one parameter and no output schema, the description provides complete contextual information: what is returned (domains, rank, engines), what is excluded ('claimed domains only'), and how counting works including edge cases (answers with no evidence). It adequately prepares the agent to interpret results and use the tool 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 input schema already covers the single parameter 'domain' with a description and example, giving 100% schema description coverage. The tool description does not add additional semantic detail about the parameter itself; it focuses on output semantics. Thus, the baseline of 3 is appropriate since 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: to get the domains cited in stored answers for a cataloged domain's tracked questions, ranked by citation count and with the engines that cited them. It uses a specific verb ('Get'), identifies the resource ('cited domains'), and distinguishes itself from siblings like get_citations by focusing on domains rather than individual citations.

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 provides clear context for when to use the tool: it is for seeing which domains were cited, not for endorsements or quality rankings. It implies an alternative use case ('instead') but does not explicitly name sibling tools or state when not to use it. The counting rules and 'claimed domains only' constraint add practical guidance, but explicit exclusions are missing.

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