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AI Humanizer MCP Server

by chongzixuan-ai
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JavaScript

AI Humanize MCP Server

A powerful Model Context Protocol (MCP) server that helps refine AI-generated content to sound more natural and human-like. Built with advanced AI detection and text enhancement capabilities.

Powered by text2go.

Screenshot

screenshot

Related MCP server: Natural Voice MCP

✨ Key Features

  • 🤖 AI Detection - Accurately identify AI-generated content

  • 👤 Natural Language Enhancement - Transform robotic text into natural human-like writing

  • ⭐ Grammar Perfection - Ensure flawless grammatical accuracy

  • 📋 Readability Optimization - Improve text flow and comprehension

  • 📏 Length Control - Adjust content length while preserving meaning

  • ⚖️ Term Preservation - Maintain specific terminology and key phrases

Prerequisites

  • node version >= 16

  • Claude Desktop latest version

Installation

By npx

  1. Configure Claude Desktop:

    • Open Claude Desktop

    • Navigate to Settings → Developer → Edit Config

    • Add the following configuration:

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

  1. Restart Claude Desktop to apply changes

By Source Code

  1. Clone this repository

  2. Install Dependencies

npm install

  1. Build the project

npm run build

  1. Configure Claude Desktop:

    • Open Claude Desktop

    • Navigate to Settings → Developer → Edit Config

    • Add the following configuration:

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

  1. Restart Claude Desktop to apply changes

💡 Usage

AI Text Detection

To check if text is AI-generated, simply ask 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.

Star History

Star History Chart

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
typeYes
textYes
detectionTypeListYes

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 update
    • 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

C2.5/5.0

Scored across 1 tool

Disambiguation5/5

With only one tool named 'detect', there is no possibility of ambiguity or overlap with other tools. The tool's purpose is singular and clearly defined as detecting AI-generated text and providing a specific URL format.

Naming Consistency5/5

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

Tool Count2/5

One tool is too few for a server named 'AI Humanizer MCP Server', which suggests broader functionality like humanizing or transforming AI text. A single detection tool feels incomplete and under-scoped for the implied domain.

Completeness2/5

The server's name implies capabilities beyond detection, such as humanizing or modifying AI-generated text, but only a detection tool is provided. This creates a significant gap where agents cannot perform the core 'humanizer' function suggested by the server name.

Maintenance

ActivityInactive
ResponsivenessNo issues

Resources

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  • Natural Voice MCP

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  • Humanizer PRO

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