GPTHuman Humanizer

A Model Context Protocol (MCP) server providing access to GPTHuman's API, the leading platform for rewriting AI-generated text into more natural, human-sounding prose, with AI-detector metadata returned when available. This allows any MCP-compatible client (Cursor, Claude Desktop, etc.) to call the humanizer tool natively.

The server is shipped as a single humanize_text tool that rewrites AI-generated text into a more natural, human-sounding variant, while preserving the requested tone and rewrite mode.

Quick Start

You can run the server directly and test it in 60 seconds:

export GPTHUMAN_API_KEY=...
npx -y @gpthuman/mcp-server

Requirements

• Node.js >= 22.0.0
• A GPTHuman API key — get one at GPTHuman.ai

Configuration

The server reads a single environment variable:

Installation

Cursor

Add the server to ~/.cursor/mcp.json (or your workspace .cursor/mcp.json):

{
  "mcpServers": {
    "gpthuman": {
      "command": "npx",
      "args": ["-y", "@gpthuman/mcp-server"],
      "env": {
        "GPTHUMAN_API_KEY": "your-api-key-here"
      }
    }
  }
}

Security Note: While the example above places the GPTHUMAN_API_KEY directly in JSON, we recommend using environment variables or local secret storage when possible. Never commit .cursor/mcp.json with real API keys to version control.

Claude Desktop

Add it to claude_desktop_config.json:

{
  "mcpServers": {
    "gpthuman": {
      "command": "npx",
      "args": ["-y", "@gpthuman/mcp-server"],
      "env": {
        "GPTHUMAN_API_KEY": "your-api-key-here"
      }
    }
  }
}

Other clients

Any MCP client that supports the stdio transport can run the server with:

GPTHUMAN_API_KEY=your-api-key-here npx -y @gpthuman/mcp-server

Tools

humanize_text

Transforms AI-generated text into a more natural, human-sounding variant designed to bypass AI detectors, while preserving the requested tone and rewrite mode.

Input parameters

Output

The tool returns two content blocks:

1. The humanized text (the primary payload).
2. A markdown summary with metadata: AI-detector human score, similarity to original, readability, detected language, applied tone and mode, input/output word and character counts, credit usage, remaining credit balance, and the request ID.

Example call (from an MCP client)

{
  "name": "humanize_text",
  "arguments": {
    "text": "Your AI-generated text of at least 300 characters goes here...",
    "tone": "College",
    "mode": "Balanced"
  }
}

Example Output

---
**Metadata Summary:**
- **Human Score:** 98%
- **Similarity:** 85%
- **Readability:** College-level
- **Credit Usage:** 142
- **Remaining Balance:** 4,858
- **Request ID:** req_xyz123