NerA-MCP: The Arabic Intelligence Layer

NerA-MCP (Middle-East Network Architecture - Model Context Protocol) is a senior-engineered intelligence layer designed to bridge the gap between global Large Language Models (LLMs) and the complex linguistic, legal, and cultural nuances of the Arabic-speaking world.

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🚀 Core Value Proposition

While global models have improved in basic Arabic processing, they consistently fail at mission-critical enterprise tasks in the MENA region. NerA-MCP provides:

• Linguistic Precision: Native dialect normalization (Gulf, Egyptian, Levantine) to MSA.
• Enterprise Compliance: Automated Sharia-compliance auditing and Hijri/Gregorian temporal synchronization.
• Information Integrity: Credibility scoring for Arabic news and social media chains.
• Document Intelligence: High-accuracy Arabic OCR with stamp and seal detection.

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🛠 Features & Capabilities

🧠 Linguistic Intelligence

• Dialect Normalization: Context-aware transformation of regional dialects into Modern Standard Arabic.
• Advanced Name Analysis: intelligent parsing of compound Arabic names (e.g., merging "بن عبد الله") for KYC and legal matching.

🏛 Enterprise & Legal

• Sharia Compliance Engine: Automated auditing of contracts against AAOIFI standards (Hanafi, Maliki, Hanbali schools).
• Temporal Engine: Bi-directional Hijri-Gregorian conversion optimized for legal and government date systems.

📰 Information Trust

• News Credibility Scoring: Analysis of clickbait markers, source reputation, and sensationalism in Arabic media.
• Document Extraction: Robust OCR pipeline for scanned Iqamas, National IDs, and stamped certificates.

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🏗 System Architecture

graph TD
    A[LLM: Claude/GPT/Qwen] -->|MCP Protocol| B[NerA-MCP Server]
    B --> C{Intelligence Layer}
    C --> D[Linguistic Engine]
    C --> E[Compliance Engine]
    C --> F[OCR/Vision Pipeline]
    D -->|Dialects| G[MSA Output]
    E -->|AAOIFI| H[Audit Report]
    F -->|Tesseract| I[Structured Data]

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📦 Installation & Setup

For Developers (Local Setup)

1. Environment Configuration:

   conda create -y -n nera1 python=3.11
   conda activate nera1

2. Install Dependencies:

   pip install .

3. Deploy System-level OCR (Optional):

   sudo apt-get install tesseract-ocr

For Production (Docker)

NerA-MCP is fully containerized for reliable scaling:

docker-compose up --build -d

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🔌 Integration

Claude Desktop Configuration

Add the following to your claude_desktop_config.json:

{
  "mcpServers": {
    "ner-a-mcp": {
      "command": "conda",
      "args": [
        "run",
        "--no-capture-output",
        "-n",
        "nera1",
        "python",
        "/home/mrazek/NerA/ner_a_mcp/server.py"
      ]
    }
  }
}

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🌍 Distribution

Publish to PyPI

This project is pyproject.toml compliant. To distribute:

1. Build: python -m build
2. Upload: twine upload dist/*

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🛡 Security & Observability

• Logging: Comprehensive logging via sys.stdout with standardized timestamps.
• Error Handling: Robust exception shielding to ensure the MCP server remains resilient under high load.
• Auditability: All tool invocations are logged for enterprise auditing.

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🤝 Contributing

Contributions are welcome. Please ensure that all new tools include:

1. Comprehensive docstrings.
2. Input validation via Pydantic models.
3. Integration tests in test_tools.py.

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📜 License

Distributed under the MIT License. See LICENSE for more information.

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Made with ❤️ for the Arabic AI Ecosystem