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

Annotations already declare readOnly, openWorld, idempotent, and non-destructive, so safety is covered. The description adds real value beyond them by stating that the response includes both the LLM answer and any cited sources/links — important since there is no output schema. It omits cost/latency characteristics and whether a call is billed, which would complete the picture.

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 core capability is front-loaded before the GEO rationale and the example. The em-dash construction in the first clause is slightly convoluted, but every element — capability, purpose, example — earns its place with no padding.

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 correctly compensates by explaining that the return includes the answer text and cited sources. Combined with a 100%-covered schema and full annotations, the agent has enough to call it correctly; only billing/latency and sibling routing are 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?

Schema description coverage is 100%, so both user_prompt and model_name are already fully documented in the schema, including the retired-model warning. The description's example merely restates those parameters with a sample value rather than adding format or constraint detail, so the baseline 3 applies.

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 gives a concrete verb+resource: it runs a prompt through an LLM and returns the answer plus cited sources/links. The GEO/AI-visibility framing makes the intent unambiguous. It does not, however, distinguish itself from close siblings like ai_visibility_check or scan_competitor_ai_presence, which appear to overlap.

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?

Usage is implied through the 'see what an LLM says about a brand, topic, or query' phrasing and a worked example, which conveys the intended context. There is no explicit statement of when not to use it, nor any pointer to the competing ai_visibility_check or scan_competitor_ai_presence tools, leaving the agent to guess among siblings.

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