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

Discloses default model is free, Anthropic requires separate API key with pass-through, and return format (per-model and combined view). Annotations already indicate read-only, idempotent, non-destructive; description adds cost implications and data flow details without contradiction.

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 covering purpose, parameter usage, and use cases. No extraneous information. Front-loaded with the core function. Each sentence adds value.

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?

With 4 parameters and no output schema, the description fully explains all parameters and the return structure (per-model score, confidence, signals, raw_response + combined view). It also addresses the cost model for Anthropic. No gaps.

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 4 parameters have descriptions in the schema (100% coverage). The tool description adds meaningful context: default model, free usage, API key pass-through, and disambiguation purpose for 'context'. This goes 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?

Clearly states the action (probe LLMs), resource (business/brand/product/topic), and output (visibility score 0-100). Distinguishes from sibling tools by focusing on visibility scoring rather than general Q&A or scanning. Includes specific use cases (AI-marketing audits, pre-launch checks, competitive monitoring).

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 context on when to use (brand audits, competitive monitoring) and how to set parameters (default model, BYO key for Anthropic, context disambiguation). Does not explicitly contrast with sibling tools like 'ask_pipeworx' or 'scan_competitor_ai_presence', but the description itself gives sufficient guidance.

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