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Typosquat / Brand-Protection Variants

domain_variants
Read-onlyIdempotent

Generate typosquatting and brand-protection variants of a domain name and check which are registered in WhisperGraph. Identify lookalikes from 14 mutation algorithms for threat investigation.

Instructions

Generate typosquatting / brand-protection variants of a domain or brand name and check which ones actually exist in WhisperGraph.

Runs 14 mutation algorithms - character omission, repetition, transposition, QWERTY-adjacent replacement/insertion, vowel-swap, bitsquatting, homoglyph / Unicode confusables, hyphenation, dot insertion/omission, TLD-swap, TLD-addition, and subdomain-add. Unicode input is accepted (and expected) so IDN homoglyph lookalikes resolve correctly.

Returns { rows: [...] }. Each row: { variant, method, exists, nodeId, label, confidence (0.3-0.9), confidenceLabel (low/medium/high) }. By default only variants that EXIST as nodes are returned - the registered lookalikes worth investigating. Note that "exists" means registered/observed, NOT malicious: pivot each hit through explain_indicator for a threat verdict.

Arguments:

  • name (string, required) - the domain or brand to mutate, e.g. "google.com". Allowed characters: letters (including Unicode), digits, '.', '-', '_'.

  • label (string, optional, default HOSTNAME) - node label to check existence against.

  • includeNonExistent (boolean, optional, default false) - when true, also return generated variants that do NOT exist in the graph (larger, noisier result set).

Performance: typically <150ms. Results are capped at 500 rows.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameYesDomain or brand to generate variants for. Examples: "google.com", "paypal.com". Unicode allowed.
labelNoOptional node label to check existence against. Default: HOSTNAME.
includeNonExistentNoOptional. When true, also return generated variants that do not exist in the graph. Default: false.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
rowsYes
Behavior5/5

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

Adds value beyond annotations: performance timings, result caps, default behavior (existent-only), and clarifies that 'exists' does not imply malicious intent. 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.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Well-structured and informative, but slightly verbose with algorithm list. Could be more concise without losing essential details.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's complexity (14 algorithms), the description covers purpose, algorithms, output schema, parameters, performance, and sibling tool recommendation. Very complete.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema covers all parameters fully (100%). Description adds allowed characters, default values, and behavioral implications (e.g., includeNonExistent noisiness), enhancing beyond schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool generates typosquatting/brand-protection variants and checks existence, distinguishing it from siblings like query or explain_indicator.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Provides explicit context for use (brand-protection, threat investigation) and suggests pivoting to explain_indicator, but lacks explicit when-not-to-use or comparison to alternative tools.

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

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