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Text2Go

AI Humanizer MCP Server

by Text2Go
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JavaScript
Remote

AI人性化MCP服务器

强大的模型上下文协议 (MCP) 服务器，可帮助优化 AI 生成的内容，使其听起来更自然、更贴近人类。内置先进的 AI 检测和文本增强功能。

由text2go提供支持。

目录

  1. 主要特点

  2. 截屏

  3. 先决条件

  4. 安装

  5. 用法

截屏

截屏

Related MCP server: Natural Voice MCP

✨ 主要特点

  • 🤖 AI 检测- 准确识别 AI 生成的内容

  • 👤自然语言增强——将机器人文本转化为自然的人类写作

  • ⭐语法完美- 确保语法完美无缺

  • 📋可读性优化——改善文本流程和理解能力

  • 📏长度控制- 调整内容长度，同时保留含义

  • ⚖️术语保留- 保留特定术语和关键短语

先决条件

  • 节点版本> = 16

  • Claude桌面最新版本

安装

作者：npx

  1. 配置Claude桌面：

    • 打开 Claude 桌面

    • 导航至“设置”→“开发者”→“编辑配置”

    • 添加以下配置：

{
  "mcpServers": {
    "ai-humanizer": {
      "command": "npx",
      "args": ["-y", "ai-humanizer-mcp-server"]
    }
  }
}

  1. 重新启动 Claude Desktop 以应用更改

按源代码

  1. 克隆此存储库

  2. 安装依赖项

npm install

  1. 构建项目

npm run build

  1. 配置Claude桌面：

    • 打开 Claude 桌面

    • 导航至“设置”→“开发者”→“编辑配置”

    • 添加以下配置：

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

  1. 重新启动 Claude Desktop 以应用更改

💡 使用方法

人工智能文本检测

要检查文本是否由 AI 生成，只需询问 Claude：

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.

星史

星空历史图

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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