MCP Database Server

mcp-db-server

 

An MCP (Model Context Protocol) server that exposes relational databases (PostgreSQL/MySQL) to AI agents with natural language query support. Transform natural language questions into SQL queries and get structured results.

Features

• Multi-Database Support: Works with PostgreSQL and MySQL
• Natural Language to SQL: Convert plain English queries to SQL using HuggingFace transformers
• RESTful API: Clean FastAPI-based endpoints for database operations
• Safety First: Read-only operations with query validation and result limits
• Docker Ready: Complete containerization with Docker Compose
• Production Ready: Health checks, logging, and error handling
• AI Agent Friendly: Designed specifically for AI agent integration

API Endpoints

| Endpoint | Method | Description | | --------------------------------- | ------ | -------------------------------------------- | | /health | GET | Health check and service status | | /mcp/list_tables | GET | List all available tables with column counts | | /mcp/describe/{table_name} | GET | Get detailed schema for a specific table | | /mcp/query | POST | Execute natural language queries | | /mcp/tables/{table_name}/sample | GET | Get sample data from a table |

Quick Start

Option 1: Docker Compose (Recommended)

1. Clone and start the services:

   git clone https://github.com/Souhar-dya/mcp-db-server.git
   cd mcp-db-server
   docker-compose up --build

1. Test the endpoints:

   # Health check
   curl http://localhost:8000/health

   # List tables
   curl http://localhost:8000/mcp/list_tables

   # Describe a table
   curl http://localhost:8000/mcp/describe/customers

   # Natural language query
   curl -X POST "http://localhost:8000/mcp/query" \
     -H "Content-Type: application/json" \
     -d '{"nl_query": "show top 5 customers by total orders"}'

Option 2: Local Development

1. Prerequisites:

• Python 3.11+
• PostgreSQL or MySQL database

1. Install dependencies:

   pip install -r requirements.txt

1. Set environment variables:

   export DATABASE_URL="postgresql+asyncpg://user:password@localhost:5432/dbname"
   # or for MySQL:
   # export DATABASE_URL="mysql+pymysql://user:password@localhost:3306/dbname"

1. Run the server:

   python -m app.server

Sample Database

The project includes a sample database with realistic e-commerce data:

• customers: Customer information (10 sample customers)
• orders: Order records (17 sample orders)
• order_items: Individual items within orders
• order_summary: View combining order and customer data

Natural Language Query Examples

The server can understand various types of natural language queries:

# Get all customers
curl -X POST "http://localhost:8000/mcp/query" \
  -H "Content-Type: application/json" \
  -d '{"nl_query": "show all customers"}'

# Count orders by status
curl -X POST "http://localhost:8000/mcp/query" \
  -H "Content-Type: application/json" \
  -d '{"nl_query": "count orders by status"}'

# Top customers by order value
curl -X POST "http://localhost:8000/mcp/query" \
  -H "Content-Type: application/json" \
  -d '{"nl_query": "top 5 customers by total order amount"}'

# Recent orders
curl -X POST "http://localhost:8000/mcp/query" \
  -H "Content-Type: application/json" \
  -d '{"nl_query": "show recent orders from last week"}'

Configuration

Environment Variables

| Variable | Description | Default | | -------------- | ---------------------------- | ---------------------------------------------------------------- | | DATABASE_URL | Full database connection URL | postgresql+asyncpg://postgres:postgres@localhost:5432/postgres | | DB_HOST | Database host | localhost | | DB_PORT | Database port | 5432 | | DB_USER | Database username | postgres | | DB_PASSWORD | Database password | postgres | | DB_NAME | Database name | postgres | | HOST | Server host | 0.0.0.0 | | PORT | Server port | 8000 |

Database Connection Examples

# PostgreSQL
DATABASE_URL=postgresql+asyncpg://user:pass@localhost:5432/mydb

# MySQL
DATABASE_URL=mysql+pymysql://user:pass@localhost:3306/mydb

# PostgreSQL with SSL
DATABASE_URL=postgresql+asyncpg://user:pass@localhost:5432/mydb?sslmode=require

### Database Connection Examples

PostgreSQL (local or cloud)

DATABASE_URL=postgresql+asyncpg://user:password@host:5432/dbname

MySQL (local or cloud)

DATABASE_URL=mysql+aiomysql://user:password@host:3306/dbname

PostgreSQL with SSL (cloud, e.g. Neon, Supabase, Aiven)

DATABASE_URL=postgresql+asyncpg://user:password@host:5432/dbname?sslmode=require

MySQL with SSL (cloud, e.g. Aiven, PlanetScale)

DATABASE_URL=mysql+aiomysql://user:password@host:3306/dbname?ssl-mode=REQUIRED ```

Note: - For MySQL cloud providers, the ssl-mode parameter in the URL is ignored by the driver, but SSL is always enabled in the MCP server for cloud connections. - For PostgreSQL, use sslmode=require for cloud DBs. For MySQL, just use the standard URL; SSL is handled automatically. - If you see errors about ssl-mode or sslmode, check your URL and ensure you are using the correct driver prefix (mysql+aiomysql or postgresql+asyncpg).

Cloud Database Examples

# Neon (PostgreSQL)
DATABASE_URL=postgresql+asyncpg://username:password@ep-xxxxxx-pooler.us-east-2.aws.neon.tech/dbname

# Aiven (MySQL)
DATABASE_URL=mysql+aiomysql://avnadmin:yourpassword@mysql-xxxxxx-username-xxxx.aivencloud.com:11079/defaultdb?ssl-mode=REQUIRED

Docker Usage with Cloud DB

docker run -d \
  -p 8000:8000 \
  -e DATABASE_URL="<your_cloud_database_url>" \
  souhardyak/mcp-db-server:latest

Troubleshooting

• If you get connect() got an unexpected keyword argument 'ssl-mode', ignore it: SSL is still enabled.
• For network errors, check firewall and DB credentials.
• For MySQL, always use mysql+aiomysql in the URL for async support.

## Security Features

- **Read-Only Operations**: Only SELECT queries are allowed
- **Query Validation**: Automatic detection and blocking of dangerous SQL operations
- **Result Limiting**: Maximum 50 rows per query (configurable)
- **Input Sanitization**: Protection against SQL injection
- **Safe Defaults**: Secure configuration out of the box

## Architecture

mcp-db-server/ ├── app/ │ ├── __init__.py # Package initialization │ ├── server.py # FastAPI application and endpoints │ ├── db.py # Database connection and operations │ └── nl_to_sql.py # Natural language to SQL conversion ├── .github/workflows/ │ └── docker-publish.yml # CI/CD pipeline ├── docker-compose.yml # Docker Compose configuration ├── Dockerfile # Container definition ├── init_db.sql # Sample database schema and data ├── requirements.txt # Python dependencies └── README.md # This file ```

Model Context Protocol (MCP) Integration

This server is designed to work seamlessly with MCP-compatible AI agents:

1. Standardized Endpoints: RESTful API following MCP conventions
2. Structured Responses: JSON responses optimized for AI consumption
3. Error Handling: Consistent error messages and status codes
4. Documentation: OpenAPI/Swagger documentation available at /docs

Publish To VS Code MCP Store (Registry)

VS Code MCP gallery uses MCP Registry metadata. This repository now includes server.json for registry publication.

1) Build and publish Docker image

docker build -t souhardyak/mcp-db-server:1.3.1 .
docker push souhardyak/mcp-db-server:1.3.1

2) Validate server metadata

server.json is configured for an OCI package and stdio transport:

• name: io.github.Souhar-dya/mcp-db-server
• registryType: oci
• identifier: docker.io/souhardyak/mcp-db-server:1.3.1

The Dockerfile includes registry ownership annotation:

• io.modelcontextprotocol.server.name=io.github.Souhar-dya/mcp-db-server

3) Publish to MCP Registry

Install publisher and publish metadata:

mcp-publisher login github
mcp-publisher publish

After publishing, users can discover/install it from MCP-compatible clients, including VS Code MCP experiences that read from the registry.

4) Local VS Code config example

{
  "servers": {
    "mcp-db-server": {
      "type": "stdio",
      "command": "docker",
      "args": [
        "run",
        "--rm",
        "-i",
        "-e",
        "DATABASE_URL=sqlite+aiosqlite:////data/default.db",
        "souhardyak/mcp-db-server:1.3.1"
      ]
    }
  }
}

Docker Smoke Test

Use the dedicated Docker smoke test in tests/docker:

python tests/docker/smoke_test.py

This verifies Docker daemon access, image build, container startup, and health status.

Deployment

Docker Hub

# Pull the latest image
docker pull souhardyak/mcp-db-server:latest

# Run with your database
docker run -d \
  -p 8000:8000 \
  -e DATABASE_URL="your_database_url_here" \
  souhardyak/mcp-db-server:latest

Kubernetes

apiVersion: apps/v1
kind: Deployment
metadata:
  name: mcp-db-server
spec:
  replicas: 3
  selector:
    matchLabels:
      app: mcp-db-server
  template:
    metadata:
      labels:
        app: mcp-db-server
    spec:
      containers:
        - name: mcp-db-server
          image: souhardyak/mcp-db-server:latest
          ports:
            - containerPort: 8000
          env:
            - name: DATABASE_URL
              valueFrom:
                secretKeyRef:
                  name: db-secret
                  key: url
---
apiVersion: v1
kind: Service
metadata:
  name: mcp-db-server-service
spec:
  selector:
    app: mcp-db-server
  ports:
    - port: 80
      targetPort: 8000
  type: LoadBalancer

Testing

Run Tests Locally

# Start test database
docker-compose up postgres -d

# Wait for database to be ready
sleep 10

# Run tests
python -m pytest tests/ -v

Manual Testing

# Test health endpoint
curl http://localhost:8000/health

# Test table listing
curl http://localhost:8000/mcp/list_tables

# Test natural language query
curl -X POST "http://localhost:8000/mcp/query" \
  -H "Content-Type: application/json" \
  -d '{"nl_query": "show me all customers from California"}'

Contributing

1. Fork the repository
2. Create your feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add some amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

License

This project is licensed under the Apache License 2.0 - see the LICENSE file for details.

📝 Changelog

v1.3.0 (2025-12-24) - Docker Path Fix

• Fixed: Resolved import path issues in Docker container causing from db import DatabaseManager to fail
• Fixed: Changed relative paths to absolute paths in Dockerfile and docker-compose.yml healthchecks
• Improved: mcp_server.py now uses robust path resolution that works both locally and in Docker containers
• Updated: Docker image rebuilt and pushed with all path fixes

v1.2.0 (2025-11-03) - MySQL Column Access Fix

• Fixed: Resolved Could not locate column in row for column 'column_name' error with MySQL databases
• Fixed: Changed describe_table method to use index-based row access for better SQLAlchemy compatibility
• Improved: Enhanced cross-database compatibility for schema introspection
• Resolved: GitHub Issue #1

v1.1.0 (2025-09-28) - Async Bug Fix

• Fixed: Resolved str can't be used in 'await' expression error in MCP server
• Improved: NLP query processing now works correctly with Claude Desktop integration
• Enhanced: Added comprehensive test database setup scripts
• Updated: Docker image rebuilt with bug fixes and updated dependencies

v1.0.0 (2025-09-25) - Initial Release

• Initial: Full MCP Database Server implementation
• Added: RESTful API with FastAPI
• Added: Natural language to SQL conversion
• Added: Docker containerization and deployment
• Added: Multi-database support (PostgreSQL, MySQL, SQLite)

Acknowledgments

• FastAPI for the excellent web framework
• HuggingFace Transformers for NL to SQL capabilities
• SQLAlchemy for database abstraction
• The Model Context Protocol (MCP) community

Support

• Report Issues
• Discussions
• Documentation

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