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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It describes a read-only operation (matching and returning) and implies no side effects. While it doesn't explicitly state read-only or discuss edge cases, the 'returns' language and the nature of the tool make the intent clear. This is adequate for a lookup tool.

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?

Three concise sentences: the first gives the trigger condition, the second explains the action and output, and the third names alternatives. Every sentence earns its place with no redundancy or fluff.

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 single-parameter lookup tool with no output schema, the description is complete: it covers intended use, behavior, and alternatives. Nothing an agent needs to call it correctly is missing.

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 schema already fully documents the single 'stack' parameter. The description restates the stack types but adds no new semantic detail beyond what the schema provides. Baseline 3 is appropriate when the schema carries the descriptive burden.

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 matches a named stack against 53 published LeadProof guides and returns canonical paths and concise steps. It also explicitly distinguishes itself from audit_lead_workflow and evaluate_leadproof_fit, which focus on reliability/ROI, so an agent can easily tell them apart.

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?

It explicitly says 'Use when the buyer names an automation, form, AI voice, ad, or CRM stack and needs implementation guidance' and then directs to alternatives for reliability scoring or ROI. This is a clear when-to-use and when-not-to-use with named sibling tools.

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