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Text2Go

AI Humanizer MCP Server

by Text2Go
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JavaScript
Remote

AI Humanize MCP 서버

AI가 생성한 콘텐츠를 더욱 자연스럽고 인간적인 느낌으로 다듬는 데 도움이 되는 강력한 모델 컨텍스트 프로토콜(MCP) 서버입니다. 고급 AI 감지 및 텍스트 향상 기능을 탑재하고 있습니다.

text2go 로 구동됩니다.

목차

  1. 주요 특징

  2. 스크린샷

  3. 필수 조건

  4. 설치

  5. 용법

스크린샷

스크린샷

Related MCP server: Natural Voice MCP

✨ 주요 특징

  • 🤖 AI 감지 - AI가 생성한 콘텐츠를 정확하게 식별

  • 👤 자연어 향상 - 로봇 텍스트를 자연스러운 인간형 글쓰기로 변환

  • ⭐ 문법 완벽성 - 완벽한 문법 정확성을 보장합니다

  • 📋 가독성 최적화 - 텍스트 흐름과 이해도 향상

  • 📏 길이 조절 - 의미를 보존하면서 콘텐츠 길이를 조절합니다.

  • ⚖️ 용어 보존 - 특정 용어 및 핵심 문구 유지

필수 조건

  • 노드 버전 >= 16

  • Claude Desktop 최신 버전

설치

npx에 의해

  1. Claude Desktop 구성:

    • 클로드 데스크톱 열기

    • 설정 → 개발자 → 구성 편집으로 이동합니다.

    • 다음 구성을 추가합니다.

지엑스피1

  1. 변경 사항을 적용하려면 Claude Desktop을 다시 시작하세요.

소스 코드로

  1. 이 저장소를 복제하세요

  2. 종속성 설치

npm install

  1. 프로젝트를 빌드하세요

npm run build

  1. Claude Desktop 구성:

    • 클로드 데스크톱 열기

    • 설정 → 개발자 → 구성 편집으로 이동합니다.

    • 다음 구성을 추가합니다.

{
  "mcpServers": {
    "ai-humanizer": {
      "command": "node",
      "args": ["<YOUR_PROJECT_PATH>/build/index.js"]
    }
  }
}

  1. 변경 사항을 적용하려면 Claude Desktop을 다시 시작하세요.

💡 사용법

AI 텍스트 감지

텍스트가 AI가 생성한 것인지 확인하려면 Claude에게 다음과 같이 물어보세요.

Is this text ai-generated:  In a fast-paced world, where technology is advancing at an exponential rate, it is crucial for businesses to adapt to new trends and keep up with the changing demands of the market.

스타 역사

별 역사 차트

Available Tools

1 tool
detectD

Detect whether the text is AI-generated.Show to user the task detail url. Extract the taskId field, then concatenate the link in the following format: https://pre-www.text2go.ai/?utm_source=claude_mcp&taskId={taskId}

ParametersJSON Schema
NameRequiredDescriptionDefault
detectionTypeListYes
textYes
typeYes

TDQS

D1.8/5.0
Behavior2/5

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

With no annotations provided, the description carries full burden for behavioral disclosure. It mentions showing a task detail URL and extracting/concatenating a taskId, which suggests this tool performs both detection AND URL generation. However, it doesn't disclose what happens after detection (e.g., returns a score, classification, confidence), whether it makes external API calls, rate limits, or authentication requirements.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness2/5

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

The description is poorly structured - it starts with the core purpose but immediately mixes in implementation details about URL formatting. The second sentence about extracting taskId and concatenating links feels like internal implementation instructions rather than a clear tool description. It's not front-loaded with essential information.

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

Completeness2/5

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

For a 3-parameter detection tool with no annotations and no output schema, the description is incomplete. It doesn't explain what the tool returns (just mentions showing a URL), doesn't clarify the detection mechanism, and doesn't provide context about the detectionTypeList options (COPYLEAKS vs HEMINGWAY). The URL formatting details seem like implementation noise rather than helpful context.

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

Parameters2/5

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

Schema description coverage is 0%, so the description must compensate for undocumented parameters. The description mentions 'text' but doesn't explain what kind of text or length limits. It doesn't mention 'detectionTypeList' or 'type' parameters at all, leaving three parameters essentially unexplained beyond their schema definitions.

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

Purpose2/5

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

The description states 'Detect whether the text is AI-generated' which provides a basic purpose, but it's vague about the mechanism and immediately diverges into implementation details about URLs and task IDs. The title is null, and the description doesn't clearly distinguish this as a standalone detection tool versus part of a workflow.

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

Usage Guidelines1/5

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

No guidance on when to use this tool is provided. The description jumps straight to implementation details without explaining the context, prerequisites, or alternatives. There's no mention of when this detection would be appropriate versus other methods.

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

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections.

  1. 1 tool updatev1.0.0
    • First observeddetect
Deploy Server
One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

TDQS

C2.3/5.0

Scored across 1 tool

Disambiguation5/5

With only one tool, there is no possibility of ambiguity or overlap between tools. The single tool 'detect' has a clearly defined purpose that cannot be confused with any other tool in this set.

Naming Consistency5/5

A single tool inherently demonstrates perfect naming consistency as there are no other tools to compare against. The tool name 'detect' follows a clear verb-based pattern appropriate for its function.

Tool Count2/5

A single tool is insufficient for most server purposes, creating a thin surface that limits agent capabilities. While the tool has a specific function, the server's scope appears to be AI text detection, which would typically benefit from additional related operations like analysis, comparison, or verification tools.

Completeness2/5

The server appears focused on AI text detection, but with only a detection tool, there are significant gaps in functionality. There's no way to analyze results, compare texts, verify human-written content, or perform related operations that would complete the AI-human text analysis domain.

Maintenance

ActivityInactive
ResponsivenessNo issues

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