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

Annotations already indicate safe, read-only, idempotent behavior. The description adds behavioral details: it probes entities with ai_visibility_check, ranks results, and returns score/confidence/signal density per entity. No contradiction with 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 sentences with a parenthetical example, front-loading the purpose and mechanism. Every sentence adds value; no wasted words.

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?

The description covers what the tool does, how it works (probes, ranks), and what it returns (ranked list with metrics). Combined with schema that documents parameters and constraints (2-8 entities), it is sufficiently complete for an agent to use 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 context about entities array (first as subject, rest as competitors) which is not in schema, but other parameters like models and context are already well described in schema. Description adds only marginal value beyond schema.

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: compare AI visibility across multiple entities side-by-side. It specifies the action (compare), resource (AI visibility), and scope (multiple entities). It distinguishes itself from the sibling tool ai_visibility_check by being an aggregate comparison tool.

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 a clear use case: competitive AI-marketing audits, with an example question. It implies when to use this tool over single entity checks but does not explicitly mention when not to use or compare to other siblings like compare_entities.

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