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chongzixuan-ai

AI Humanizer MCP Server

by chongzixuan-ai
OverviewSchemaRelated ServersScoreDiscussions
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
detectC

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

C2.1/5.0
Behavior2/5

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

No annotations are provided, so the description carries the full burden. It mentions showing 'the task detail url' to the user and extracting/concatenating a taskId, which hints at some output behavior. However, it fails to describe critical aspects like what the detection result looks like, error conditions, or rate limits. The behavioral disclosure is incomplete.

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 detection purpose but then abruptly shifts to URL construction instructions without clear connection. This creates confusion rather than clarity. While brief, it fails to be effectively concise due to the disjointed content.

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?

Given the complexity (3 parameters, 0% schema coverage, no annotations, no output schema), the description is inadequate. It does not explain the parameters, the detection output, or the relationship between detection and the URL task. For a tool with no structured support, this leaves too many gaps.

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

Parameters1/5

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 all three parameters. It provides no information about what 'type', 'text', or 'detectionTypeList' mean, their expected formats, or how they influence detection. The description adds zero semantic value beyond the bare schema.

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

Purpose3/5

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

The description states the tool 'detects whether the text is AI-generated', which provides a clear purpose. However, it then confusingly adds instructions about extracting a taskId and constructing a URL, which seems unrelated to the core detection function. The purpose is somewhat vague due to this mixed messaging.

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

Usage Guidelines2/5

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

No guidance is provided on when to use this tool versus alternatives. The description does not mention any prerequisites, constraints, or appropriate contexts for invocation. With no sibling tools, this is less critical but still a gap in usage instructions.

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. Dates show when Glama detected each change.

  1. 1 tool update
    • First observeddetect
Install Server
RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

TDQS

C2.5/5.0
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
ResponsivenessSyncing

Resources

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Related MCP Connectors

Related MCP Servers

  • Natural Voice MCP

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    Provides tools and resources to detect AI-generated writing patterns and refine text for more authentic, human-like communication. It enables users to analyze phrasing via a scoring system and apply conversational guides tailored for platforms like Twitter and LinkedIn.
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  • Humanizer PRO

    khadinakbaronline
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    Humanizer PRO - The Best AI Text Humanizer MCP Server Transform AI-generated content into natural, human-sounding text that bypasses GPTZero, Turnitin, Originality.ai, Copyleaks, ZeroGPT, and other AI detectors. Undetectable AI content rewriting with Stealth, Academic, and SEO modes.
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  • HumanizerMCP

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    Detects and removes 24 types of AI writing patterns, offering dual-mode humanization (rule-based and LLM-enhaced) with tools for detection, style transfer, scoring, and batch processing.
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