xlisp/sqllite-mcp-server

🗄️ SQLite MCP Server

Query. Create. Analyze. Visualize.
A lightweight Python-based MCP server for interacting with SQLite databases.
Easily ask questions, create databases & tables, run queries, analyze data, and even visualize results directly through AI-powered workflows.

✨ Features

🔍 Natural Querying & Analysis

• Ask free-form questions about your database.
• Automatically translate natural language queries into SQL.
• Summarize and analyze datasets.

🛠️ Database & Schema Management

• Create and manage new SQLite databases.
• Define tables and schemas interactively.
• Insert, update, and delete data with simple prompts.

📊 Data Exploration

• Perform SQL queries and get results instantly.
• Generate aggregated reports and summaries.
• Explore relationships across tables.

📈 Visualization (with Visualization MCP Server)

• Send query results to the visualization MCP server.
• Create charts, plots, and dashboards directly from SQL results.
• Supports interactive analysis with Claude or other MCP clients.

🚀 Installation

Prerequisites

# Create and activate virtual environment
python3 -m venv .venv --upgrade-deps
source .venv/bin/activate

# Install MCP CLI and dependencies
pip install "mcp[cli]" httpx

# Optional: for generating mock data
pip install faker

⚡ Usage with Claude Desktop

Add the following to your claude_desktop_config.json:

config path example: /Users/xlisp/Library/Application\ Support/Claude/claude_desktop_config.json

  "sqlite-db": {
    "command": "/Users/xlisp/PyPro/sqllite-mcp-server/.venv/bin/python",
    "args": [
      "/Users/xlisp/PyPro/sqllite-mcp-server/main.py"
    ]
  },

"visualization": {
  "command": "/Users/yourname/Desktop/visualization-mcp-server/.venv/bin/python",
  "args": [
    "/Users/yourname/Desktop/visualization-mcp-server/visualization_mcp_server.py"
  ]
}

Then restart Claude Desktop, and you’ll be able to interact with SQLite directly:

• Ask questions like “What are the top 10 users by score?”
• Create tables with “Make a table for products with name, price, and category.”
• Analyze data with “Summarize average order values by month.”
• Visualize results with “Plot a bar chart of sales by category.”

🛠️ Development

• sqlite_mcp_server.py is the main entry point.
• Extend commands to support custom SQL operations or integrations.
• Combine with visualization-mcp-server for richer workflows.
• Contributions are welcome — PRs and issues encouraged!

📜 License

MIT License.