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

Annotations already declare readOnlyHint=true and destructiveHint=false, covering safety. The description adds valuable context beyond annotations: the default model (Workers AI Llama-3.3-70b, free), cost implications of passing _apiKey ("BYO key — you pay Anthropic directly"), and the return structure. This is meaningful behavioral disclosure without contradicting 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 four sentences, logically front-loaded: core capability, default model, optional key behavior, return format, and use cases. Every sentence contributes unique information; there is no fluff or repetition of schema/annotations.

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, the description covers all key aspects: purpose, parameter behavior (defaults, optional key, cost), output shape (per-model object + combined view), and use cases. There is no output schema, so the explicit return format description fills that gap. Annotations cover safety/idempotency. The description is sufficient for an agent to select and invoke this 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 all parameters are documented in the schema. The description adds extra meaning by explaining the default model when 'models' is omitted (not fully in schema) and the cost/fund-flow for _apiKey. This nudges it above the baseline of 3, though it doesn't deeply expand every parameter.

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 opens with a specific verb+resource: "Probe one or more LLMs for what they know about a business / brand / product / topic and score visibility (0-100) per model." This clearly distinguishes it from sibling tools like ask_pipeworx (which asks questions) and compare_entities (which compares entities). The scope and output are immediately clear.

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 explicitly states when to use the tool: "Useful for AI-marketing audits, pre-launch brand checks, competitive monitoring." It also provides invocation guidance (default model, optional _apiKey to include Anthropic). However, it does not name alternatives or explicitly say when not to use it, so it misses the high bar of 5.

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