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

The annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint false, establishing a safe, reversible operation. The description adds valuable context beyond annotations: the default model is free, probing Anthropic requires a user-provided API key and direct payment, and the return structure includes per-model {score, confidence, signals, raw_response} plus a combined view. It stops short of discussing rate limits or potential accuracy caveats, but the added cost and configuration transparency earn a 4.

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?

Three sentences: the first states the core purpose and output score, the second explains model options and cost, and the third lists return format and use cases. Every sentence contributes essential information, is front-loaded, and contains no fluff or redundancy.

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 no output schema, the description appropriately includes the return shape (per-model fields + combined view). It also covers the key configuration details (default free model, optional paid Anthropic key), the target entity, and typical use cases. Combined with a comprehensive schema and strong annotations, the description fully equips an agent to decide when and how to invoke the tool.

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?

All four parameters are fully described in the schema (100% coverage), so the description is not required to compensate. The description does reinforce the relationship between _apiKey and the 'models' parameter ('pass _apiKey to also probe Anthropic') and mentions the free default model, but it doesn't provide meaningful semantics beyond what the schema already explains. Baseline 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 probes one or more LLMs for knowledge about an entity and produces a visibility score (0-100) per model. The verb 'probe' is specific, the resource is explicit, and the scope (business/brand/product/topic) is well-defined. It also distinguishes itself from sibling tools like 'ask_pipeworx' (which likely addresses the platform's own knowledge) and 'scan_competitor_ai_presence' (which focuses on competitors) by targeting any arbitrary entity.

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 gives clear use contexts ('AI-marketing audits, pre-launch brand checks, competitive monitoring') and practical configuration guidance (default free model, optional Anthropic via _apiKey). However, it does not explicitly state when not to use this tool or name alternative tools for different scenarios, so it falls short of a full 5.

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