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

The description discloses important behavioral traits beyond the annotations: the default model (Workers AI Llama-3.3-70b), the free tier, the requirement to pass an Anthropic key for additional probes, and the direct billing arrangement with Anthropic ('you pay Anthropic directly'). It also outlines the return structure. This complements the readOnlyHint/openWorldHint annotations without contradicting them.

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 contributing distinct information: main purpose, default model and key handling, return value, and use cases. It is front-loaded with the most critical information and contains no fluff or repetition.

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?

Since there is no output schema, the description correctly explains the return format: 'per-model {score, confidence, signals, raw_response} + a combined view.' It also covers the model selection logic, key handling, cost implications, and target use cases. This is complete for a tool of moderate complexity, with no major gaps.

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 coverage is 100%, so the baseline is 3. The description adds value by giving concrete examples for the 'entity' parameter (e.g., 'Pipeworx', 'OpenInvoice'), clarifying that 'models' is optional with a free default, and explaining the relationship between '_apiKey' and 'models' (only needed if 'anthropic' is in models). This goes beyond the schema's formal descriptions.

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 begins with a specific verb ('Probe') and resource ('one or more LLMs'), and clearly states the output (visibility score 0-100 per model). It further scopes the tool to brands/products/topics and lists concrete use cases, distinguishing it from siblings like ask_pipeworx (which answers questions) by focusing on visibility scoring.

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 mentions when to use the tool: 'AI-marketing audits, pre-launch brand checks, competitive monitoring.' It does not, however, name alternative sibling tools or provide exclusion criteria (e.g., 'for direct Q&A use ask_pipeworx instead'), so it falls short of a full 5.

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