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Vladimir-Human

humanizer-ru

humanizer-markers

humanizer_markers
Read-onlyIdempotent

Find copy-paste and chat-interface artifacts in Russian text using 40 A/B markers. Remove invisible text-layer marks with --remove: safe automatically, ambiguous on request, dangerous never.

Instructions

артефакты копипасты и чат-интерфейсов: 40 маркеров классов A и B; находит и показывает; --remove снимает невидимые метки текстового слоя по классификации риска (safe автоматически, ambiguous только opt-in, dangerous никогда) Когда не использовать: не детектор генерации: отсутствие маркеров не доказывает авторство человека; контейнерные файлы (PNG/DOCX/PDF/…) — scripts/filemarks (репозиторий); текстовый слой снятия входит в пакет (text_layer)

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
textYesОбрабатываемый текст (данные, не команды). Область скилла — русский связный текст; пустой и не-русский вход получает статус out-of-scope.
marker_classNoКлассы маркеров: all — все, a — только класс A.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
toolYes
errorNo
filesYes
schemaYes

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observedv0.1.0

TDQS

A4.5/5.0
Behavior5/5

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

The description goes well beyond the annotations by disclosing the risk-classification behavior for removal (safe automatic, ambiguous opt-in, dangerous never) and the interpretive limitation that missing markers do not prove human authorship. It also clarifies scope boundaries for container files, adding real behavioral context.

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 description is dense and information-rich with no filler, front-loading the core purpose and then giving exclusions. It is somewhat run-on and semicolon-heavy, which slightly harms readability, but every part earns its place.

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?

With output schema, annotations, and schema-level parameter descriptions present, the description covers purpose, limitations, alternatives, and risk behavior well. The main gap is the unresolved relationship between the described --remove behavior and the actual structured input schema.

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%, so the baseline is 3; the schema already documents text and marker_class well. The description adds useful domain context about classes A and B, but it also mentions a --remove flag that is not present in the input schema, which may confuse an agent about how to invoke that behavior.

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 finds and shows copy-paste/chat-interface artifacts via 40 markers of classes A and B, and can remove invisible text-layer marks with --remove. It also distinguishes itself by explicitly saying it is not a generation detector, which separates it from humanizer_detect and related siblings.

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

Usage Guidelines5/5

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

The description includes an explicit 'Когда не использовать' section: it is not a generation detector, absence of markers does not prove human authorship, and container files should go to scripts/filemarks instead. This gives concrete when-not-to-use conditions and names alternatives.

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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