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

Annotations already cover readOnlyHint=true, idempotentHint=true, destructiveHint=false. The description adds meaningful behavioral context: it details the output fields (variant domain, mutation type, prefix TLD count, registration date) and explains the permutation generation process. It also clarifies that it checks registration status, which is a read-only behavior consistent with the annotations. No contradictions or missing side effects are apparent.

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 and well-structured: it opens with the core purpose, then lists generated variant types, then result fields, then use cases, and ends with related tools. Every sentence provides unique value; there is no fluff or repetition. The front-loaded purpose ensures immediate understanding.

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 (many mutation types and output fields), the description fully covers what it does and what it returns. The output schema exists and the description explicitly mentions the key return fields, so no need to describe the return format further. It also lists related tools and use cases, making it complete for an AI agent to decide when 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 coverage is 100%, with the single parameter 'domain' well-described in the schema ('Domain to scan (e.g. example.com)'). The description does not add new meaning to the parameter itself, but it reinforces that the domain is the input for generating permutations. Since the schema fully documents the parameter, a baseline score of 3 is appropriate.

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's function: 'Generate typosquat permutations for a domain and check which variants are registered.' It lists specific mutation types (omission, transposition, keyboard-adjacent replacement, etc.), which distinguishes it from related lookup tools like whois, dns, and ns_reverse. The verb 'Generate' and 'check' with resource 'domain' is specific and unambiguous.

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 provides clear use cases: 'Use for brand protection, phishing detection, and defensive registration planning.' It also lists related tools (nrds, ns_reverse, dns, whois, safety), which suggests alternatives for other tasks. However, it does not explicitly state when not to use this tool or differentiate it from those related tools beyond the mutation-generation focus.

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