Jeneen's MCP Agent
Provides an Arabic legal chatbot that answers common legal questions about topics such as annual leave, divorce, custody, employment rights, and rental agreements.
Enables searching Google directly from the agent, opening search results in the default browser.
Includes a Jupyter notebook for training the VAE model that generates handwritten digit images.
Click on "Install 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., "@Jeneen's MCP Agentlegal_chat ما هي حقوقي في حال الطلاق؟"
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.
🧠 Jeneen's MCP Agent
This project is a multi-functional AI agent built using FastMCP. It includes:
✅ An Arabic legal chatbot that answers common legal questions.
🔍 A Google search tool.
🧬 A Variational Autoencoder (VAE) model that generates handwritten digit images.
📁 Project Structure
├── main.py # Main script to run the MCP agent ├── chatbot.py # Arabic legal chatbot logic ├── vae_model.py # VAE model definitions (Encoder, Decoder, VAE) ├── output/ # Model checkpoints and generated images ├── data/ # MNIST dataset (auto-downloaded) ├── VAE.ipynb # Jupyter notebook for training the VAE model └── README.md # This documentation file
Related MCP server: ADK MCP Agent
⚙️ Available MCP Tools
1. legal_chat(query: str) → str
Arabic-language chatbot that responds to legal questions such as:
Annual leave
Divorce
Custody
Employment rights
Rental agreements Example: { "tool": "legal_chat", "input": "ما هي حقوقي في حال الطلاق؟" }
2. search_google(query: str) → str
Opens a Google search in the default browser. Example: { "tool": "search_google", "input": "قانون العمل الأردني" }
3. vae_generate(n_images: int) → str
Generates handwritten digit images using a trained VAE model. Returns a base64-encoded PNG image. Example: { "tool": "vae_generate", "input": { "n_images": 8 } }
How to Run Create and activate a virtual environment python -m venv .venv source .venv/bin/activate # On Windows: .venv\Scripts\activate
Install dependencies pip install -r requirements.txt (Optional) Train the VAE model using VAE.ipynb Or use the pre-trained model in: output/vae_epoch_50.pth
Run the MCP agent python main.py
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