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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and non-destructive. The description adds valuable context: the default model is free (Workers AI Llama-3.3-70b), using Anthropic requires a BYO key with direct payment to Anthropic, and the return format includes score, confidence, signals, raw_response, and a combined view. It doesn't deep-dive into failure modes, but adds meaningful cost and output behavior.

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 with no fluff. It front-loads the core purpose, then covers key parameters and use cases, and every sentence contributes new information. It's succinct while comprehensive.

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?

The tool has 4 parameters, no output schema, and moderate complexity. The description covers the return structure ('Returns per-model {score, confidence, signals, raw_response} + a combined view'), parameter behavior for models and API key, and real-world use cases. This is sufficient context for an agent to invoke the tool correctly.

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 adds value by clarifying the default model ('Default model is Workers AI Llama-3.3-70b (free)') and the relationship between _apiKey and the models parameter ('pass _apiKey to also probe Anthropic'). This enriches the schema definitions for models and _apiKey.

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's function: 'Probe one or more LLMs for what they know about a business / brand / product / topic and score visibility (0-100) per model.' It uses a specific verb ('probe') and resource (LLMs) and differentiates from siblings like ask_pipeworx by focusing on visibility scoring rather than general Q&A.

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 explicit use cases: 'Useful for AI-marketing audits, pre-launch brand checks, competitive monitoring.' It also explains when to use the optional Anthropic key and default model. However, it doesn't explicitly contrast with sibling tools like scan_competitor_ai_presence, leaving some ambiguity for similar-sounding tools.

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