Look up domain WHOIS, DNS, SSL and email security

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

With readOnlyHint=true and destructiveHint=false already in annotations, the description goes well beyond by disclosing the cost model ('$0.003 per domain analyzed, billed to your Apify account; invalid domains and failed lookups are free'), the privacy constraint ('No registrant personal data is returned'), and the technical implementation (runs the Apify actor kantolabs/domain-whois-dns-ssl-lookup with APIFY_TOKEN, maxTotalChargeUsd caps spend). These are behavioral traits the annotations do not carry and that materially affect how an agent should invoke the tool. No contradiction with 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 long but every sentence earns its place: purpose is front-loaded (report contents), then usage guidance, then the cost and privacy constraints, then technical details. The structure is logical and front-loaded with the highest-value information first. It could arguably be tightened, but no sentence is filler given the cost and security implications of this tool.

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?

This is a complex tool (7 parameters, 5 report sections, billing implications) with no output schema, so the description must carry the burden of explaining the return value. It does: the return format is described in detail (registration fields, DNS record types, SSL fields, email security score/grade, plain-English issue list). The cost model, privacy constraint, timeout behavior, and input normalization are all covered. 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 coverage is 100%, so the baseline is 3. The description adds meaning beyond the schema by explaining the billing context that makes maxTotalChargeUsd actionable (an agent can estimate spend: 1000 domains × $0.003 = $3) and by describing the full report structure that the include* booleans toggle. It also clarifies input flexibility ('Accepts domains, URLs or email addresses') beyond the schema's bare array of strings.

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 states a specific verb ('look up') and a well-scoped resource (domain WHOIS, DNS, SSL, email security) and enumerates the exact data returned: registration data via RDAP/WHOIS, all DNS record types, SSL certificate details, and email security with score/grade. This specificity clearly differentiates it from siblings like check_domain_authority (authority score) and detect_website_tech_stack (tech stack detection), so an agent can pick it correctly without opening schemas.

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 gives explicit use-case guidance: "Use it for 'who is the registrar / when does it expire / is this domain available / is the certificate valid / who hosts their email / is DMARC set up' questions, lead enrichment and domain or certificate monitoring." This is clear when-to-use context. However, it never states when NOT to use it or names alternative sibling tools, leaving exclusions to inference.

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