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Text2Go

AI Humanizer MCP Server

by Text2Go
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JavaScript
Remote

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.

Table of Contents

  1. Key Features

  2. Screenshot

  3. Prerequisites

  4. Installation

  5. Usage

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
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. Dates show when Glama detected each change.

  1. 1 tool updatev1.0.0
    • First observeddetect
Install Server
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TDQS

C2.3/5.0
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
ResponsivenessSyncing

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