Database Query Server

An MCP (Model Context Protocol) server that lets a VS Code AI agent query databases through a structured, step-by-step workflow.

Supported Databases

• SQLite — via Python's built-in sqlite3 (always available)
• PostgreSQL — via psycopg2 (optional — install psycopg2-binary)
• MySQL — via mysql-connector-python (optional)
• Microsoft SQL Server — via pyodbc (optional — requires ODBC drivers)

Note: Only SQLite support is required to get started. The server loads database adapters lazily — missing optional drivers won't prevent the server from starting.

Setup

1. Install dependencies

pip install -r requirements.txt

2. Configure connections

Edit config.json to add your database connections:

{
  "connections": [
    {
      "name": "My Postgres",
      "type": "postgres",
      "host": "localhost",
      "port": 5432,
      "database": "mydb",
      "username": "user",
      "password": "pass"
    },
    {
      "name": "Local SQLite",
      "type": "sqlite",
      "database": "./data/local.db"
    }
  ]
}

3. (Optional) Seed the sample SQLite database

python seed_sample_db.py

This creates data/sample.db with sample customers, products, orders, and order items.

4. Register in VS Code

Add the server to your VS Code MCP settings (.vscode/mcp.json in the workspace root):

{
  "servers": {
    "database-query": {
      "command": "python",
      "args": ["server.py"],
      "cwd": "${workspaceFolder}"
    }
  }
}

Important: ${workspaceFolder} must resolve to the directory containing server.py. If this project is inside a subfolder of your workspace, adjust to "${workspaceFolder}/MCP Server".

5. Start the MCP Server in VS Code

The server needs to be started by VS Code before the tools become available:

1. Open the Command Palette (Ctrl+Shift+P)
2. Run "MCP: List Servers" — you should see database-query
3. If it shows as stopped, select it and choose Start

Alternatively, run "MCP: Restart Server" and pick database-query.

Once the server is running, the prompt and tools (get_connections, list_tables, get_table_schema, query_table) will be available to the AI agent.

MCP Tools

The server exposes four tools, designed to be used in order:

#	Tool	Purpose
1	get_connections	List available database connections (no passwords exposed)
2	list_tables	List all user tables in a database
3	get_table_schema	Get column details for a specific table
4	query_table	Execute a read-only SELECT query

Security

• Database passwords are never exposed in tool responses.
• Only SELECT / WITH / EXPLAIN statements are allowed — all write and DDL operations are rejected.
• Query results are capped at 500 rows.

Project Structure

MCP Server/
├── config.json            # Database connection configuration
├── prompt.md              # Design / prompt spec
├── seed_sample_db.py      # Creates sample SQLite database
├── server.py              # Main MCP server entry point
├── requirements.txt
├── README.md
├── data/
│   └── sample.db          # Sample SQLite database (created by seed script)
├── db/
│   ├── __init__.py
│   ├── connection.py      # Connection factory (lazy-loads adapters)
│   ├── postgres.py        # PostgreSQL adapter
│   ├── mysql.py           # MySQL adapter
│   ├── sqlite_adapter.py  # SQLite adapter
│   └── mssql.py           # MSSQL adapter
└── tools/
    ├── __init__.py
    ├── get_connections.py
    ├── list_tables.py
    ├── get_table_schema.py
    └── query_table.py