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

Beyond the annotations (readOnlyHint, openWorldHint, idempotentHint, destructiveHint=false), the description adds key behavioral details: the default model is free, using Anthropic incurs external costs, the API key is passed straight through, and it returns per-model and combined response structures. This gives the agent a clear picture of side effects and requirements.

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 purpose, then model-specific and usage details. Every sentence contributes valuable information with no redundancy or filler.

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 no output schema, the description adequately covers the return format ('per-model {score, confidence, signals, raw_response} + a combined view'), default behavior, and common use cases. It provides enough context for an agent to invoke the tool correctly without needing additional documentation.

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 parameters are already well-documented. The description adds slight value by explaining the default model and the necessity of _apiKey for Anthropic, but does not redefine or expand on the schema's parameter 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 starts with a specific verb-resource pair: 'Probe one or more LLMs for what they know about a business / brand / product / topic' and explains the scoring output (0-100 per model). It clearly differentiates from sibling tools like ask_pipeworx or deep_research by focusing on visibility scoring across multiple LLMs.

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 context for when to use the tool: 'Useful for AI-marketing audits, pre-launch brand checks, competitive monitoring.' It also explains model selection (default Workers AI, optional Anthropic with key) but does not explicitly state when not to use it or name alternative tools.

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