Multi-Database MCP Server (by Legion AI)

A server that helps people access and query data in databases using the Legion Query Runner with integration of the Model Context Protocol (MCP) Python SDK.

Start Generation Here

This tool is provided by Legion AI. To use the full-fledged and fully powered AI data analytics tool, please visit the site. Email us if there is one database you want us to support.

End Generation Here

Why Choose Database MCP

Database MCP stands out from other database access solutions for several compelling reasons:

Unified Multi-Database Interface: Connect to PostgreSQL, MySQL, SQL Server, and other databases through a single consistent API - no need to learn different client libraries for each database type.
AI-Ready Integration: Built specifically for AI assistant interactions through the Model Context Protocol (MCP), enabling natural language database operations.
Zero-Configuration Schema Discovery: Automatically discovers and exposes database schemas without manual configuration or mapping.
Database-Agnostic Tools: Find tables, explore schemas, and execute queries with the same set of tools regardless of the underlying database technology.
Secure Credential Management: Handles database authentication details securely, separating credentials from application code.
Simple Deployment: Works with modern AI development environments like LangChain, FastAPI, and others with minimal setup.
Extensible Design: Easily add custom tools and prompts to enhance functionality for specific use cases.

Whether you're building AI agents that need database access or simply want a unified interface to multiple databases, Database MCP provides a streamlined solution that dramatically reduces development time and complexity.

Features

Multi-database support - connect to multiple databases simultaneously
Database access via Legion Query Runner
Model Context Protocol (MCP) support for AI assistants
Expose database operations as MCP resources, tools, and prompts
Multiple deployment options (standalone MCP server, FastAPI integration)
Query execution and result handling
Flexible configuration via environment variables, command-line arguments, or MCP settings JSON
User-driven database selection for multi-database setups

Supported Databases

Database	DB_TYPE code
PostgreSQL	pg
Redshift	redshift
CockroachDB	cockroach
MySQL	mysql
RDS MySQL	rds_mysql
Microsoft SQL Server	mssql
Big Query	bigquery
Oracle DB	oracle
SQLite	sqlite

We use Legion Query Runner library as connectors. You can find more info on their api doc.

What is MCP?

The Model Context Protocol (MCP) is a specification for maintaining context in AI applications. This server uses the MCP Python SDK to:

Expose database operations as tools for AI assistants
Provide database schemas and metadata as resources
Generate useful prompts for database operations
Enable stateful interactions with databases

Installation & Configuration

Required Parameters

For single database configuration:

DB_TYPE: The database type code (see table above)
DB_CONFIG: A JSON configuration string for database connection

For multi-database configuration:

DB_CONFIGS: A JSON array of database configurations, each containing:
- db_type: The database type code
- configuration: Database connection configuration
- description: A human-readable description of the database

The configuration format varies by database type. See the API documentation for database-specific configuration details.

Installation Methods

Option 1: Using UV (Recommended)

When using uv, no specific installation is needed. We will use uvx to directly run database-mcp.

UV Configuration Example (Single Database):

json

REPLACE DB_TYPE and DB_CONFIG with your connection info.
{
    "mcpServers": {
      "database-mcp": {
        "command": "uvx",
        "args": [
          "database-mcp"
        ],
        "env": {
          "DB_TYPE": "pg",
          "DB_CONFIG": "{\"host\":\"localhost\",\"port\":5432,\"user\":\"user\",\"password\":\"pw\",\"dbname\":\"dbname\"}"
        },
        "disabled": true,
        "autoApprove": []
      }
    }
}

UV Configuration Example (Multiple Databases):

config.json

{
    "mcpServers": {
      "database-mcp": {
        "command": "uvx",
        "args": [
          "database-mcp"
        ],
        "env": {
          "DB_CONFIGS": "[{\"id\":\"pg_main\",\"db_type\":\"pg\",\"configuration\":{\"host\":\"localhost\",\"port\":5432,\"user\":\"user\",\"password\":\"pw\",\"dbname\":\"postgres\"},\"description\":\"PostgreSQL Database\"},{\"id\":\"mysql_data\",\"db_type\":\"mysql\",\"configuration\":{\"host\":\"localhost\",\"port\":3306,\"user\":\"root\",\"password\":\"pass\",\"database\":\"mysql\"},\"description\":\"MySQL Database\"}]"
        },
        "disabled": true,
        "autoApprove": []
      }
    }
}

Option 2: Using PIP

Install via pip:

Terminal

pip install database-mcp

PIP Configuration Example (Single Database):

config.json

{
  "mcpServers": {
    "database": {
      "command": "python",
      "args": [
        "-m", "database_mcp", 
        "--repository", "path/to/git/repo"
      ],
      "env": {
        "DB_TYPE": "pg",
        "DB_CONFIG": "{\"host\":\"localhost\",\"port\":5432,\"user\":\"user\",\"password\":\"pw\",\"dbname\":\"dbname\"}"
      }
    }
  }
}

Running the Server

Production Mode

bash

python mcp_server.py

Configuration Methods

Environment Variables (Single Database)

server.ts

export DB_TYPE="pg"  # or mysql, postgresql, etc.
export DB_CONFIG='{"host":"localhost","port":5432,"user":"username","password":"password","dbname":"database_name"}'
uv run src/database_mcp/mcp_server.py

Environment Variables (Multiple Databases)

server.ts

export DB_CONFIGS='[{"id":"pg_main","db_type":"pg","configuration":{"host":"localhost","port":5432,"user":"username","password":"password","dbname":"database_name"},"description":"PostgreSQL Database"},{"id":"mysql_users","db_type":"mysql","configuration":{"host":"localhost","port":3306,"user":"root","password":"pass","database":"mysql"},"description":"MySQL Database"}]'
uv run src/database_mcp/mcp_server.py

If you don't specify an ID, the system will generate one automatically based on the database type and description:

server.ts

export DB_CONFIGS='[{"db_type":"pg","configuration":{"host":"localhost","port":5432,"user":"username","password":"password","dbname":"database_name"},"description":"PostgreSQL Database"},{"db_type":"mysql","configuration":{"host":"localhost","port":3306,"user":"root","password":"pass","database":"mysql"},"description":"MySQL Database"}]'
# IDs will be generated as something like "pg_postgres_0" and "my_mysqldb_1"
uv run src/database_mcp/mcp_server.py

Command Line Arguments (Single Database)

bash

python mcp_server.py --db-type pg --db-config '{"host":"localhost","port":5432,"user":"username","password":"password","dbname":"database_name"}'

Command Line Arguments (Multiple Databases)

bash

python mcp_server.py --db-configs '[{"id":"pg_main","db_type":"pg","configuration":{"host":"localhost","port":5432,"user":"username","password":"password","dbname":"database_name"},"description":"PostgreSQL Database"},{"id":"mysql_users","db_type":"mysql","configuration":{"host":"localhost","port":3306,"user":"root","password":"pass","database":"mysql"},"description":"MySQL Database"}]'

Note that you can specify custom IDs for each database using the id field, or let the system generate them based on database type and description.

Multi-Database Support

When connecting to multiple databases, you need to specify which database to use for each query:

Use the list_databases tool to see available databases with their IDs
Use get_database_info to view schema details of databases
Use find_table to locate a table across all databases
Provide the db_id parameter to tools like execute_query, get_table_columns, etc.

Database connections are managed internally as a dictionary of DbConfig objects, with each database having a unique ID. Schema information is represented as a list of table objects, where each table contains its name and column information.

The select_database prompt guides users through the database selection process.

Schema Representation

Database schemas are represented as a list of table objects, with each table containing information about its columns:

config.json

[
  {
    "name": "users",
    "columns": [
      {"name": "id", "type": "integer"},
      {"name": "username", "type": "varchar"},
      {"name": "email", "type": "varchar"}
    ]
  },
  {
    "name": "orders",
    "columns": [
      {"name": "id", "type": "integer"},
      {"name": "user_id", "type": "integer"},
      {"name": "product_id", "type": "integer"},
      {"name": "quantity", "type": "integer"}
    ]
  }
]

This representation makes it easy to programmatically access table and column information while keeping a clean hierarchical structure.

Exposed MCP Capabilities

Resources

Resource	Description
`resource://schema/{database_id}`	Get the schemas for one or all configured databases

Tools

Tool	Description
`execute_query`	Execute a SQL query and return results as a markdown table
`execute_query_json`	Execute a SQL query and return results as JSON
`get_table_columns`	Get column names for a specific table
`get_table_types`	Get column types for a specific table
`get_query_history`	Get the recent query history
`list_databases`	List all available database connections
`get_database_info`	Get detailed information about a database including schema
`find_table`	Find which database contains a specific table
`describe_table`	Get detailed description of a table including column names and types
`get_table_sample`	Get a sample of data from a table

All database-specific tools (like execute_query, get_table_columns, etc.) require a db_id parameter to specify which database to use.

Prompts

Prompt	Description
`sql_query`	Create an SQL query against the database
`explain_query`	Explain what a SQL query does
`optimize_query`	Optimize a SQL query for better performance
`select_database`	Help user select which database to use

Development

Using MCP Inspector

run this to start the inspector

Terminal

npx @modelcontextprotocol/inspector uv run src/database_mcp/mcp_server.py

then in the command input field, set something like

Code

run src/database_mcp/mcp_server.py --db-type pg --db-config '{"host":"localhost","port":5432,"user":"username","password":"password","dbname":"database_name"}'

Testing

bash

uv pip install -e ".[dev]"
pytest

Publishing

bash

# Clean up build artifacts
rm -rf dist/ build/ 
# Remove any .egg-info directories if they exist
find . -name "*.egg-info" -type d -exec rm -rf {} + 2>/dev/null || true
# Build the package
uv run python -m build
# Upload to PyPI
uv run python -m twine upload dist/*

License

This repository is licensed under GPL