MCP Data Assistant

📄 MCP schema 🔖 Latest release

Data Assistant MVP v0.5 – a fully-local Model Context Protocol server that lets any modern LLM:

• run_sql – safely query a SQLite database
• summarise_csv – get quick statistics from a CSV file
• create_pdf – turn any dict into a one-page PDF report
• Assistant – natural language interface with GPT-4.1 mini agent or local qwen3:8b model

Quick start

python -m venv .venv && source .venv/bin/activate
pip install -r requirements.txt

# Option 1: Use with OpenAI API
export OPENAI_API_KEY=your_api_key
python app.py          # open http://localhost:7860

# Option 2: Use with local qwen3:8b (see Edge AI setup below)
# No API key required!
python app.py

# CLI demo (requires OpenAI API key)
export OPENAI_API_KEY=your_api_key
./scripts/demo_cli.py "Show me total sales for 2024 and create a PDF report"

Docker

docker build -t neurark/mcp-data-assistant .
docker run -p 7860:7860 neurark/mcp-data-assistant
# open http://localhost:7860

▶ Demo — open sample_docs/report-demo-new.pdf

Advanced PDF reports

The PDF tool now supports additional fields:

• cover – adds a logo to the first page
• summary – short text displayed under the title
• chart_spec – customize chart color and size A minimal example:

{
  "title": "Sales Overview",
  "summary": "Highlights for 2024",
  "cover": {"logo_path": "assets/logo.png"},
  "sections": [
    {"title": "Intro", "type": "paragraph", "text": "Quarterly results"},
    {
      "title": "Revenue",
      "type": "chart",
      "chart_spec": {"chart_type": "bar", "labels": ["Q1", "Q2"], "values": [1, 2], "color": "#ff8800"}
    }
  ]
}

Run create_pdf with this JSON to produce a multi-page report with a styled chart. The data used for this demo is available at sample_docs/report-demo.json.

How it works

The app launches Gradio with mcp_server=True.
The LLM discovers three tools via the MCP schema and chains them as needed (query → analyse → generate report).

The Assistant tab provides a natural language interface allowing users to interact with the tools through conversational prompts. It supports two model options:

• OpenAI API with GPT-4.1 mini model (requires API key)
• Local qwen3:8b model via Ollama (no API key required)

Built with Python 3.12, Gradio 5.29, SQLModel, Pandas, ReportLab, OpenAI Agents SDK, and Ollama.

Edge AI Capability (v0.5)

MCP Data Assistant now supports a local qwen3:8b model using Ollama:

1. Install Ollama from ollama.ai
2. Run: ollama pull qwen3:8b (downloads the 8B parameter model, ~5GB)
3. Make sure Ollama is running: ollama serve (if not already running)
4. Start the app and select "Local (qwen3:8b)" in the interface

No API key required when using the local model! The qwen3:8b model supports multilingual requests, reasoning, mathematics, and function calling.

Troubleshooting:

• If you encounter errors, make sure Ollama is running by executing ollama serve in a separate terminal
• If you get API errors, try restarting the application
• qwen3:8b requires at least 12GB of RAM for optimal performance