AI Content Detector MCP Server
Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@AI Content Detector MCP ServerIs this text AI-generated?"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
🔍 AI Content Detector MCP Server
Detect AI-generated content, analyze writing style, check plagiarism, and humanize text. Recognizes 24 AI writing patterns. Sister product of HumanizerMCP - forms a "detect + humanize" closed loop.
Features
8 MCP Tools: detect_ai_content, analyze_writing_style, check_plagiarism, get_detection_report, humanize_content, batch_detect, compare_versions, get_ai_probability
24 AI Patterns: Based on Wikipedia "Signs of AI writing" guide
Style Analysis: Sentence rhythm, vocabulary diversity, personal voice, emotional richness
Plagiarism Check: N-gram similarity and self-repetition detection
Humanization: Actionable suggestions to reduce AI traces
Quick Start
# Install
pip install -r requirements.txt
# Run (stdio mode)
python server.py
# Run (HTTP mode)
python server.py --transport http --port 8010Tools
Tool | Description |
| Detect AI patterns with severity scores |
| Analyze sentence patterns and voice |
| Check similarity and self-repetition |
| Comprehensive detection report |
| Suggestions to make text more human |
| Detect multiple texts at once |
| Compare AI scores between versions |
| Quick AI probability score |
AI Patterns Detected
🔴 High Severity: Elevated vocabulary, vague attribution, promotional tone, boilerplate intro, lack of specificity, buzzword density, no personal anecdotes, parallel negation
🟡 Medium Severity: Em dash overuse, rule of three, transition overuse, conclusion summary, hedge words, redundant phrasing, over-explanation, neutral stance, generic examples
🟢 Low Severity: Perfect grammar, list-heavy, template structure, symmetric structure, emotional flatness
Integration with HumanizerMCP
This detector pairs perfectly with HumanizerMCP:
Detect → Identify AI patterns and their severity
Humanize → Apply fixes using HumanizerMCP's rule/LLM engine
Compare → Verify AI score reduction with
compare_versions
MCP Config
{
"mcpServers": {
"ai-content-detector": {
"command": "python",
"args": ["server.py"],
"cwd": "/path/to/ai-content-detector-mcp"
}
}
}在线访问(推荐)
无需本地安装,直接在MCP客户端配置:
{
"mcpServers": {
"ai-content-detector-mcp": {
"url": "http://www.mzse.com/detector-mcp/"
}
}
}REST API
curl http://www.mzse.com/detector-mcp/部署状态
License
MIT
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