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

The description fully complements the annotations (readOnlyHint, idempotentHint, etc.) by explaining the cost implications of using the Anthropic model and detailing the return structure. There is 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 concise and well-structured. It front-loads the primary function, then clarifies defaults, optional features, return format, and use cases. Every sentence adds value without unnecessary details.

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?

Given the tool's moderate complexity and lack of output schema, the description covers all necessary aspects: input parameters, behavior per model, return structure, and example use cases. It is complete and actionable for an AI agent.

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?

With 100% schema coverage, the baseline is 3. The description adds value by clarifying the default model, explaining the optional API key usage, and describing how the context parameter helps disambiguate. This exceeds mere schema repetition.

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 includes specific use cases like 'AI-marketing audits, pre-launch brand checks, competitive monitoring' which distinguishes it from sibling tools (e.g., ask_pipeworx, scan_competitor_ai_presence).

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 explains when to use the tool (for visibility checks) and provides context for optional features like adding an API key for Anthropic. However, it does not explicitly state when not to use it or contrast it with alternatives, which would improve clarity.

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