mcp-ai-detection

Open-source MIT MCP server for multi-tier AI-detection screening on academic papers. It accepts .tex and .docx, extracts clean text, splits standard paper sections, and runs a three-tier risk pipeline.

AI detection is screening, not proof. Reports include limits, threats to validity, and a final recommendation framed as decision support.

Features

• MCP tools: extract_text, split_sections, full_pipeline

• Input: LaTeX .tex and Word .docx

• Text extraction: Pandoc for LaTeX when installed, robust fallback cleaner, python-docx for Word

• Narrative/structured split: tables, formulas, captions, references, keyword lines, markdown tables, and dense math lines are excluded from the main authorship score

• Section splitting: Abstract, Introduction, Methods, Results, Discussion, Conclusion

• Tier 1 offline: burstiness, lexical diversity, AI-like connectives, n-gram repetition, sentence-length variance, repeated patterns, hedging, example density

• Optional Tier 1 local LLM through Ollama with gemma4:e4b by default

• Tier 2 local Gemma adjudicator through Ollama: rubric-based JSON screening calibrated with Tier 1 metrics, no paid API keys

• Tier 3 open-source ensemble hooks: DetectGPT, Fast-DetectGPT, NPR command adapters plus built-in proxy analysis for repetition, lexical diversity, and semantic coherence

• JSON and Markdown reports with executive summary, section breakdown, section x tier score table, narrative score, structured-content diagnostic, limits, and recommendation

Related MCP server: biolit

Install

python -m pip install -e .

Pandoc is optional but recommended for LaTeX:

# macOS
brew install pandoc

# Ubuntu/Debian
sudo apt-get install pandoc

MCP server

Run with stdio transport:

python -m mcp_ai_detection.server

Example MCP client config:

{
  "mcpServers": {
    "ai-detection": {
      "command": "python",
      "args": ["-m", "mcp_ai_detection.server"],
      "env": {
        "LOCAL_LLM_MODEL": "gemma4:e4b"
      }
    }
  }
}

Tools

extract_text

{
  "file_path": "paper.tex",
  "prefer_pandoc": true
}

Returns clean text, word count, extractor used, and warnings.

split_sections

{
  "text": "Abstract\n...\nIntroduction\n..."
}

Returns detected standard sections with line ranges and word counts.

full_pipeline

{
  "file_path": "paper.docx",
  "use_llm": false,
  "tier2_provider": "gemma-local",
  "early_stop": true
}

Runs extraction, sectioning, Tier 1 statistics, conditional Tier 2 Gemma/Ollama, conditional Tier 3, then returns report_json and report_markdown.

CLI

python -m mcp_ai_detection.cli paper.tex --markdown report.md --json report.json

Configuration

Environment variables:

LOCAL_LLM_MODEL=gemma4:e4b
OLLAMA_HOST=http://localhost:11434
OLLAMA_KEEP_ALIVE=30m
HTTP_TIMEOUT_SECONDS=120
TIER1_LLM_WEIGHT=0.6
TIER1_STATS_WEIGHT=0.4

DETECTGPT_CMD=
FAST_DETECTGPT_CMD=
NPR_CMD=
METHODS_WEIGHT_REDUCTION=0.75

Tier 2 uses the local Ollama model named by LOCAL_LLM_MODEL. Recommended:

ollama pull gemma4:e4b
ollama serve

Check that Ollama is using the GPU:

ollama ps

The PROCESSOR column should show 100% GPU for loaded models.

External Tier 3 commands receive section text on stdin and should return JSON:

{
  "score": 0.72,
  "confidence": 0.64,
  "details": {
    "model": "your-detector"
  }
}

If commands are not configured, built-in proxy scorers keep the pipeline fully offline and deterministic.

Thresholds

• < 0.3: low

• 0.3-0.6: medium

• >= 0.6: high

• Tier 2 early stop: probability < 0.4

• Sections below 80 narrative words are marked insufficient_evidence and are excluded from the document-level narrative score

Methods sections get reduced Tier 3 weight by default to lower false positives from formulaic scientific prose.

Development

Run offline tests:

python -m unittest discover -s tests

Run lint if dev extras are installed:

ruff check .

gemma3:4b is a smaller fallback for slower machines:

LOCAL_LLM_MODEL=gemma3:4b