labeveryday/writestat-mcp

WriteStat MCP Server

MCP server for text readability analysis and AI pattern detection. Helps writers identify AI-like patterns and improve readability.

Created by Du'An Lightfoot | @labeveryday

Installation

pip install writestat-mcp

# Optional: ML-based detection (~500MB for torch/transformers)
pip install writestat-mcp[ml]

# Required: NLTK data
python -c "import nltk; nltk.download('punkt_tab')"

Tools

Claude Desktop Setup

Add to ~/Library/Application Support/Claude/claude_desktop_config.json (macOS) or %APPDATA%/Claude/claude_desktop_config.json (Windows):

{
  "mcpServers": {
    "readability-mcp": {
      "command": "uvx",
      "args": ["writestat-mcp"]
    }
  }

With Claude Code

# After PyPI publish
# Pattern detection only (lightweight)
claude mcp add writestat-mcp -- uvx writestat-mcp

# Or with ML detection (~500MB download)
claude mcp add writestat-mcp -- uvx "writestat-mcp[ml]"

# From local source
cd /path/to/writestat-mcp
pip install -e .
claude mcp add writestat-mcp -- writestat-mcp

Example Prompts

Full analysis workflow:

I just wrote this blog post. Check the readability, find any difficult sentences, and flag anything that sounds too AI-generated. Then suggest improvements:

Editing pass:

This is my draft and my revised version. Compare them and tell me if the readability improved and if I removed the AI-sounding phrases. {First_draft} vs {second_draft}

Quick AI check:

Does this paragraph have any AI tells? Be specific about which phrases to fix:

Target audience check:

I'm writing for high school students. Is this text at the right reading level? Which sentences are too complex:

AI Detection: What to Expect

This tool uses heuristic pattern matching and zero-shot perplexity scoring—not a fine-tuned classifier.

How It Works

• Pattern detection: Catches stylistic markers (em dashes, filler phrases, buzzwords)
• ML detection: Measures perplexity, vocabulary diversity, burstiness

Accuracy Context

Research shows fine-tuned RoBERTa models achieve ~99% F1 on ChatGPT detection (Guo et al., 2023). Our lightweight approach won't match that. It's designed for:

• Quick pattern screening
• Catching obvious AI tells
• Educational awareness about AI writing patterns

Not suitable for: Academic integrity decisions, high-stakes verification

What the Research Found

The HC3 paper identified key ChatGPT markers we detect:

• Lower perplexity (more predictable) ✓
• Lower vocabulary diversity ✓
• Formal conjunctions ("Furthermore", "It's important to note") ✓
• Organized structure with clear transitions ✓

Score Interpretation

Readability (Flesch-Kincaid Grade)

AI Probability (ML)

Requirements

• Python 3.10+
• Core: fastmcp, textstat, nltk
• Optional [ml]: torch, transformers

License

MIT

References

• HC3: How Close is ChatGPT to Human Experts? - Guo et al., 2023
• Model Context Protocol
• FastMCP