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

Annotations already declare safe, idempotent, read-only behavior. Description adds significant value by detailing the internal process (probes each entity with ai_visibility_check, ranks by score) and output structure (ranked list with score, confidence, signal density). No contradictions.

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?

Two sentences, front-loaded with main action, no filler. Every sentence adds essential information, including a concrete example and output details.

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?

Despite no output schema, description fully describes the return value. All 4 parameters are covered (schema + description). Complexity is moderate and well addressed. Nothing critical is 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 coverage is 100% with detailed descriptions. Description adds contextual meaning: 'entities' first entry is subject, rest competitors; explains default models and API key requirement. This enhances understanding beyond schema, so a 4 is justified.

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?

Description clearly states it compares AI visibility across multiple entities side-by-side, ranks by score, and surfaces most/least recognized. Distinguishes from siblings like ai_visibility_check (single entity) via explicit method (probes each with that tool) and multi-entity comparison.

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?

Provides a concrete use case: competitive AI-marketing audits with an example question. Implicitly clear when to use it (comparing multiple entities), but doesn't explicitly exclude alternatives like compare_entities. Still strong guidance.

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