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

Discloses cost implications (Anthropic calls require BYO key and direct payment), return structure per-model with {score, confidence, signals, raw_response} and combined view. Annotations already indicate read-only, idempotent, safe; description adds valuable behavioral context beyond 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?

Single paragraph efficiently conveys purpose, usage, and return format. Could be slightly more structured (e.g., bullet points for return), but no 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?

No output schema, but description fully explains return structure. Covers all 4 parameters, including optional parameters and their roles. Provides sufficient context for agent to understand tool scope and limitations.

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%, and description adds meaning: default model (free Workers AI), _apiKey purpose, context disambiguation. Provides examples for entity parameter, enhancing 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 verb 'probe', resource 'LLMs', and outcome 'score visibility (0-100) per model'. Distinguishes from siblings by focusing on visibility scoring across multiple models, unlike general Q&A tools like ask_pipeworx or deep_research.

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?

Explicitly mentions use cases: AI-marketing audits, pre-launch brand checks, competitive monitoring. Explains when to use _apiKey for Anthropic and default model. Does not explicitly state when not to use, but context is clear.

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