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

Annotations already declare read only, idempotent, and open world hints. The description adds behavioral detail beyond that: it reveals the tool probes each entity with ai_visibility_check, ranks by score, and returns confidence and signal density per entity. It does not contradict 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 four concise sentences with no redundancy. It front-loads the core action, then explains the process, provides a use case, and lists return fields. Every sentence earns its place.

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?

Even without an output schema, the description specifies the return elements (score, confidence, signal density per entity), explains the multi-entity workflow, and gives a concrete use case. Combined with full parameter descriptions and annotations, it is complete for the tool's complexity.

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 describes all four parameters with 100% coverage, so the description adds little extra meaning. It mentions entities ('your brand + N competitors') and the probe mechanism, but this largely mirrors the schema. A baseline of 3 is appropriate.

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 'Compare AI visibility across multiple entities side-by-side', names the underlying probe mechanism (ai_visibility_check), and describes the ranking and output. It distinguishes itself from sibling ai_visibility_check (single entity) and compare_entities (generic comparison) by focusing specifically on AI visibility and competitive audits.

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?

It provides concrete context: 'Useful for competitive AI-marketing audits' and gives an example query. It implies use when comparing multiple entities, but stops short of explicitly stating when not to use it or suggesting alternative tools, hence a 4 rather than 5.

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