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

The description discloses behavioral traits beyond annotations: default model is Workers AI Llama-3.3-70b (free), Anthropic requires a BYO key and incurs direct costs, and it returns per-model results plus a combined view. Annotations already indicate readOnlyHint, idempotentHint, etc., and the description adds operational context without contradiction.

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 (two sentences plus a brief list for return structure) and front-loaded with the primary action and default behavior. Every sentence serves a purpose: explaining the tool, how to extend it, and what results to expect. No unnecessary words.

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 complexity (4 parameters, no output schema), the description provides sufficient information: it describes the return schema (per-model objects with score, confidence, signals, raw_response plus combined view), default model, and optional parameters. No output schema exists, so the description adequately compensates. The agent has enough context to select 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?

The input schema has 100% description coverage for all four parameters. The tool description adds value by explaining the role of each parameter in context: `entity` is the thing to ask about, `models` lists which to probe, `_apiKey` is optional for Anthropic, `context` helps disambiguate. This exceeds the baseline of 3 for high coverage, earning a 4.

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 uses specific verbs ('probe', 'score') and identifies the resource ('LLMs for what they know about a business / brand / product / topic'). It clearly distinguishes from sibling tools like 'ask_pipeworx' which likely answer questions rather than score visibility. The return structure (score, confidence, signals per model) further clarifies the tool's purpose.

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 use cases: 'AI-marketing audits, pre-launch brand checks, competitive monitoring.' It also provides guidance on optional parameters (e.g., `_apiKey` required for Anthropic, `context` for disambiguation). While it doesn't explicitly state when not to use, the context of sibling tools implies alternative functions. A score of 4 reflects clear, actionable guidance.

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