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

Annotations already indicate read-only, open-world, idempotent behavior. The description adds valuable context beyond this: the default model is free, passing _apiKey routes calls to Anthropic with direct billing to the user, and the return structure is disclosed ('per-model {score, confidence, signals, raw_response} + a combined view'). 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 three sentences, front-loaded with the core action, and every sentence adds meaningful information (purpose, default behavior, return format, use cases). No redundancy or fluff.

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, the description discloses return structure, use cases, default behavior, and billing nuances. For a tool with 4 parameters and one required, this is sufficiently complete for an agent to select and invoke it correctly. Minor omissions like rate limits don't detract significantly.

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 description coverage is 100%, so the baseline is 3. The description enhances parameter understanding by explaining the default model behavior for the 'models' parameter and the billing implication of '_apiKey', plus the disambiguation purpose of 'context'. This adds value 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 ('Probe') and clearly defines the resource ('one or more LLMs for what they know about a business / brand / product / topic') and the output ('score visibility (0-100) per model'). It distinctly differentiates from sibling tools like ask_pipeworx or deep_research by focusing on AI visibility scoring.

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 clear context with explicit use cases ('AI-marketing audits, pre-launch brand checks, competitive monitoring') and explains the default model and optional Anthropic key. However, it does not explicitly state when not to use this tool or mention alternative tools for comparison, so it lacks exclusionary guidance.

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