GlyphGuard

Check a name for lookalike impersonation

compare_names

Read-onlyIdempotent

Check whether a name, username, domain label or brand looks the same as any of up to 50 protected names (for example "pаypal" with a Cyrillic а vs "paypal"), using a UTS #39-style confusable skeleton (curated subset, case and accents ignored). Read-only. Same engine as GET /api/compare.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "name": "<name>",
      "targets": "<targets>"
    },
    "name": "compare_names"
  }
}

Input Schema

Name	Required	Description	Default
`name`	Yes	Name, username, domain label or brand to test.
`targets`	Yes	1 to 50 protected names.

Output Schema

Name	Required	Description	Default
`name`	No
`notes`	No
`results`	Yes
`version`	Yes
`nameRisk`	No
`skeleton`	Yes
`nameScripts`	No
`confusableWith`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedOct 22, 2026

TDQS

A4.1/5.0

Behavior4/5

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

Annotations already declare readOnly/idempotent/non-destructive, so 'Read-only' is redundant, but the description adds genuinely useful behavior: the UTS #39-style confusable skeleton, that it is a curated subset, and that case and accents are ignored. This tells the agent what counts as a match and where recall may be limited.

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?

Core purpose is front-loaded in the first clause, with caveats parenthesized. The parenthetical example and algorithm notes are dense but each earns its place; 'Read-only' duplicates the annotation slightly.

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

Completeness4/5

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

An output schema exists so return values need no explanation, and annotations cover the safety profile. The description supplies matching semantics and limits; the only gap is sibling differentiation against clean_text/scan_text.

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 coverage is 100% so baseline is 3, but the description adds semantic detail beyond the schema: the 50-target cap is surfaced and the normalization rules (case/accent-insensitive confusable skeleton) clarify what values 'name' really compares on.

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 concrete verb ('check whether... looks the same') with the resource (name/username/domain label/brand) and the comparison target (up to 50 protected names). The homoglyph example ('pаypal' with Cyrillic а) makes the operation immediately distinguishable from the sibling text tools.

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

Usage Guidelines3/5

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

The impersonation-check framing implies the use case, but there is no explicit when-to-use/when-not guidance and no routing to clean_text or scan_text. The 'Same engine as GET /api/compare' note is provenance, not selection guidance.

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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