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

Annotations already declare the tool as read-only, idempotent, and non-destructive. The description adds behavioral context beyond annotations: it mentions the default model, that Anthropic calls require a BYO key with direct payment, and the structure of the return value. This provides useful behavioral insight not covered by annotations alone.

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 four sentences, each serving a distinct purpose: main action, configuration details, return type, and use cases. No filler, all content is essential and front-loaded.

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?

For a tool with 4 parameters and no output schema, the description covers the main purpose, parameter specifics, return structure (including fields like score, confidence, signals, raw_response), and use cases. It lacks details on how the scoring works or what the signals are, but overall it provides sufficient context for an AI agent to understand and 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?

Input schema has 100% parameter description coverage, so baseline is 3. The description adds value by explaining the default model for the 'models' parameter and clarifying the role of '_apiKey' (BYO key for Anthropic). This enriches the schema definitions without redundancy.

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 probes LLMs for knowledge about a business/product/topic and returns a visibility score. It specifies the verb 'Probe' and the resource 'LLMs', and distinguishes itself from sibling tools by focusing on AI visibility scoring, which is unique among the listed tools.

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 provides explicit use cases ('AI-marketing audits, pre-launch brand checks, competitive monitoring') and explains the default model and optional Anthropic key, guiding when to use each. However, it does not directly contrast with sibling tools or state when not to use it.

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