Database MCP Server (by Legion AI)

Database MCP Server (by Legion AI)

By TheRaLabs GitHub

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

database mcp
Overview

What is Legion MCP?

Legion MCP (Model Context Protocol) Server is a server designed to facilitate access and querying of data in databases using the Legion Query Runner, integrated with the Model Context Protocol (MCP) Python SDK.

How to use Legion MCP?

To use Legion MCP, set up the server by installing dependencies, configuring your database connection, and running the server in either development or production mode. You can execute queries and manage database operations through the MCP interface.

Key features of Legion MCP?

  • Database access via Legion Query Runner
  • Support for Model Context Protocol (MCP) for AI assistants
  • Exposes database operations as MCP resources, tools, and prompts
  • Multiple deployment options including standalone and FastAPI integration
  • Flexible configuration through environment variables and command-line arguments

Use cases of Legion MCP?

  1. Enabling AI assistants to interact with databases seamlessly.
  2. Executing complex SQL queries and retrieving results in various formats.
  3. Managing database schemas and metadata for AI applications.

FAQ from Legion MCP?

  • What is the Model Context Protocol (MCP)?

MCP is a specification for maintaining context in AI applications, allowing for stateful interactions with databases.

  • How do I install Legion MCP?

Follow the installation instructions provided in the documentation, which includes setting up a virtual environment and installing dependencies.

  • Can I run Legion MCP in production?

Yes, Legion MCP can be run in production mode with appropriate configurations.

Content

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.

End Generation Here

Features

  • 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

Supported Databases

DatabaseDB_TYPE code
PostgreSQLpg
Redshiftredshift
CockroachDBcockroach
MySQLmysql
RDS MySQLrds_mysql
Microsoft SQL Servermssql
Big Querybigquery
Oracle DBoracle
SQLitesqlite

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

Two parameters are required for all installation methods:

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

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

Installation Methods

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

UV Configuration Example:






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": []
      }
    }
}

Option 2: Using PIP

Install via pip:

pip install database-mcp

PIP Configuration Example:

{
  "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

Development Mode

mcp dev mcp_server.py

Production Mode

python mcp_server.py

Configuration Methods

Environment Variables

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

Command Line Arguments

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

Or with UV:

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

Exposed MCP Capabilities

Resources

ResourceDescription
schema://allGet the complete database schema

Tools

ToolDescription
execute_queryExecute a SQL query and return results as a markdown table
execute_query_jsonExecute a SQL query and return results as JSON
get_table_columnsGet column names for a specific table
get_table_typesGet column types for a specific table
get_query_historyGet the recent query history

Prompts

PromptDescription
sql_queryCreate an SQL query against the database
explain_queryExplain what a SQL query does
optimize_queryOptimize a SQL query for better performance

Development

Testing

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

Publishing

rm -rf dist/ build/ *.egg-info/ && python -m build
python -m build
python -m twine upload dist/*

License

This repository is licensed under GPL

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