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@gpthuman/mcp-server
Advanced tools
Model Context Protocol (MCP) server providing access to GPTHuman's API, the leading platform for transforming AI-generated text into natural, human-sounding content that successfully bypasses AI detectors.
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.
You can run the server directly and test it in 60 seconds:
export GPTHUMAN_API_KEY=...
npx -y @gpthuman/mcp-server
To test the server interactively with the MCP Inspector before wiring it up to Cursor or Claude:
npx @modelcontextprotocol/inspector npx -y @gpthuman/mcp-server
>= 22.0.0The server reads a single environment variable:
| Variable | Required | Description |
|---|---|---|
GPTHUMAN_API_KEY | Yes | Your GPTHuman API key. |
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_KEYdirectly in JSON, we recommend using environment variables or local secret storage when possible. Never commit.cursor/mcp.jsonwith real API keys to version control.
Add it to claude_desktop_config.json:
{
"mcpServers": {
"gpthuman": {
"command": "npx",
"args": ["-y", "@gpthuman/mcp-server"],
"env": {
"GPTHUMAN_API_KEY": "your-api-key-here"
}
}
}
}
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
humanize_textTransforms 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
| Name | Type | Required | Default | Description |
|---|---|---|---|---|
text | string | Yes | — | The text to humanize. Must be at least 300 characters and at most 2,000 words. |
tone | enum | No | College | Target reading level / tone. One of Standard, HighSchool, College, PhD. |
mode | enum | No | Balanced | Rewrite strategy. One of Professional, Balanced, Enhanced. |
Output
The tool returns two content blocks:
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
Once the server is configured, try giving your AI agent prompts like:
If you don’t want to run the MCP server locally, you can call GPTHuman’s hosted MCP endpoint directly over HTTP using JSON-RPC 2.0. This is useful for custom agents, backend workflows, automation platforms, or internal tools that want to integrate GPTHuman without managing a local MCP process.
https://api.gpthuman.ai/mcp
Use tools/list to inspect the tools exposed by the GPTHuman MCP server.
curl --location 'https://api.gpthuman.ai/mcp' \
--header 'Content-Type: application/json' \
--header 'Accept: application/json' \
--data '{
"jsonrpc": "2.0",
"method": "tools/list",
"id": 1
}'
Use tools/call with the humanize_text tool to transform AI-generated text into more natural, human-sounding writing.
curl --location 'https://api.gpthuman.ai/mcp' \
--header 'Content-Type: application/json' \
--header 'Accept: application/json' \
--data '{
"jsonrpc": "2.0",
"id": 1,
"method": "tools/call",
"params": {
"name": "humanize_text",
"arguments": {
"text": "Your AI-generated text of at least 300 characters goes here...",
"tone": "DESIRED_TONE",
"mode": "DESIRED_MODE",
"apiKey": "YOUR_GPTHUMAN_API_KEY"
}
}
}'
text: The text you want to humanize.tone: The writing tone to use, such as College, Professional, or another supported tone.mode: The humanization mode, such as Balanced.apiKey: Your GPTHuman API key.Use the remote MCP endpoint if you are building:
For desktop MCP clients like Claude Desktop, Cursor, or Windsurf, you can still use the local MCP server setup shown above.
GPTHUMAN_API_KEY is set correctly.humanScore: null: The detector score is unavailable for that specific language or content type.git clone https://github.com/GPTHuman-ai/mcp-server.git
cd mcp-server
npm install
cp .env.example .env
# Edit .env and set GPTHUMAN_API_KEY
npm run build
npm start
Available scripts:
| Script | Description |
|---|---|
npm run build | Compile TypeScript to dist/. |
npm start | Run the compiled server on the stdio transport. |
npm run format | Format the codebase with Prettier. |
npm test | Run the Jest test suite. |
src/
stdio.ts Entry point — wires the server to the stdio transport.
McpServerFactory.ts Builds the McpServer and registers tools.
GptHumanAPI.ts Wrapper around the GPTHuman REST API.
HttpsClient.ts Thin axios wrapper with auth and timeout.
type.d.ts Shared request/response interfaces.
Apache-2.0 — see LICENSE.
FAQs
Model Context Protocol (MCP) server providing access to GPTHuman's API, the leading platform for transforming AI-generated text into natural, human-sounding content that successfully bypasses AI detectors.
We found that @gpthuman/mcp-server demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 2 open source maintainers collaborating on the project.
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