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

No annotations are provided, so the description carries the full burden. It usefully discloses the x402 M2M cost (0.02 USDC on Base) and the shape of the output, but says nothing about authentication, rate limits, failure modes, or whether enrichment is read-only against the target domain.

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?

Front-loaded with the tool's identity and organized into Trigger / Input / Output / Cost segments, so an agent can skim to the relevant part. The parenthetical cost note is compact rather than wasteful.

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, no-output-schema tool, the description covers trigger, input format, output shape, and pricing, which is close to complete. Remaining gaps are behavioral details (auth, limits) rather than anything about how to invoke it.

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% and there is only one parameter, so the schema already documents 'domain'. The description repeats the example (stripe.com) and adds the word 'required', adding little beyond what structured data provides; baseline 3 applies.

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?

Names a specific capability (B2B lead enrichment) and enumerates the concrete data it returns: executive contacts, headcount, LinkedIn URLs, and tech stack for a corporate domain. This distinguishes it from generic scrapers like linkedin_scraper or smartscrape_ai, though it does not explicitly name them as alternatives.

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 'Trigger' clause gives an explicit condition for use: when an agent needs verified executive contacts and company data for a corporate domain. It stops short of stating when NOT to use it or naming sibling alternatives, so it is clear context without exclusions.

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