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

Annotations (readOnlyHint, openWorldHint, idempotentHint, destructiveHint) already cover safety, and the description adds valuable context: default model, cost implications of passing _apiKey, the per-model return structure, and the combined view. It does not mention rate limits or failure behavior, but the source of extras justifies a score above baseline yet not a full 5.

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 main action and purpose, then concise details on models, return format, and use cases. Every sentence contributes information; no fluff or redundancy.

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 there is no output schema, the description fully explains the return structure (per-model {score, confidence, signals, raw_response} + combined view). It also covers parameter usage, optional setup, cost implications, and use cases, making it complete for a 4-parameter tool with external API calls.

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 semantic value by clarifying the default model (Workers AI Llama-3.3-70b), the relationship between models and _apiKey, and the direct billing model for Anthropic calls, which goes beyond what the schema descriptors state.

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 a specific action ('Probe one or more LLMs') and resource ('what they know about a business / brand / product / topic'), plus the outcome ('score visibility (0-100) per model'). It distinguishes itself from sibling tools like ask_pipeworx by emphasizing visibility scoring rather than question answering, and explicitly lists use cases like AI-marketing audits and competitive monitoring.

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 on when to use the tool ('Useful for AI-marketing audits, pre-launch brand checks, competitive monitoring') and explains optional setup (_apiKey for Anthropic). However, it doesn't explicitly name alternatives or state when not to use it, relying on implied differentiation rather than explicit exclusions.

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