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

Annotations already indicate safe read-only, idempotent behavior. The description adds value by explaining the internal mechanism (probing each entity with `ai_visibility_check`) and the output structure (ranked list with score, confidence, signal density). This discloses key behavioral traits beyond the annotations.

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 two front-loaded sentences: the first clearly states the core function, and the second adds context and output details. Every sentence earns its place without waste.

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 4 parameters (1 required) and no output schema, the description adequately describes the output format and the required parameter's role. Optional parameters are covered by the schema. The description is complete enough for an agent to select and invoke 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?

Schema coverage is 100%, so baseline is 3. The description adds semantic value by specifying that the first entity is treated as the 'subject' for narrative purposes and explaining the `context` parameter's disambiguation role. This provides meaning beyond the schema descriptions.

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 uses a specific verb ('Compare') and resource ('AI visibility across multiple entities'), differentiating it from siblings like `ai_visibility_check` (single entity) and `compare_entities` (generic). It provides a concrete use case and output example, making the purpose unmistakable.

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 explicitly states the tool is useful for 'competitive AI-marketing audits' and gives an example question, implying when to use it. It does not explicitly list alternatives or exclusions, but the context from sibling names (e.g., `ai_visibility_check`) and the tool's comparative nature provide sufficient guidance.

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