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OpenBait lookalike domains

Check lookalike domains

check_lookalike_domains
Read-onlyIdempotent

Find lookalike domains of a brand's website: the brand's name with common words such as login or pay, on the domain's own ending (or on .com for endings like .co.jp), and, for most longer brand names, .shop names of the kind copied stores use. Checks which of them resolve in DNS and what their front page shows (a page using the brand's name, a redirect to the brand's own site, another live site, or nothing). It does not try misspellings or look-alike characters. Read-only. Same check as https://openbait.com/tools/typosquat-check.

Input Schema

NameRequiredDescriptionDefault
domainYesthe brand's website, e.g. example.com or https://www.example.com

Output Schema

NameRequiredDescriptionDefault
domainYesthe registrable domain that was checked
summaryYes
resultUrlYesthe same result on openbait.com
lookalikesYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.2/5.0
Behavior4/5

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

Annotations already declare readOnlyHint, openWorldHint and idempotentHint, so the safety profile is carried structurally. The description adds real behavioral context beyond that: it explains that results are DNS-resolution outcomes plus a classification of the front page (brand-name page, redirect to brand, other live site, nothing), which tells the agent what 'found' means. It does not mention rate limits, throttling, or cost, so not a 5.

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?

The core behavior and the negative scope are front-loaded within the first two sentences, and every clause carries information. The first sentence is nonetheless long and clause-dense, which makes it slower to parse than it needs to be.

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?

An output schema exists and the description still characterizes the shape of results in plain terms, plus scope limits and read-only status. For a single-parameter lookup tool, nothing an agent needs to decide to call it or interpret results is missing.

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

Parameters3/5

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

Schema coverage is 100% and the single 'domain' parameter is already documented with acceptable formats (example.com or https://www.example.com). The description clarifies that this is the brand's own site rather than the lookalike, which is mild added value, but the schema does the heavy lifting, so baseline 3 applies.

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?

States a specific verb (find) and resource (lookalike domains of a brand's website) and then enumerates exactly which generation patterns are covered: brand name plus login/pay, on the same ending or .com, plus .shop variants. It also carves out what it will not do (misspellings, look-alike characters), so the boundary of the tool is unambiguous.

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?

Gives clear context for use (typosquat/lookalike domain triage for a brand) and an explicit exclusion ('does not try misspellings or look-alike characters'), which tells an agent when the result set is incomplete. There are no sibling tools to route against and no prerequisites or rate-limit guidance, which keeps it below a 5.

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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