Legion MCP (Model Context Protocol) Server

Legion MCP (Model Context Protocol) Server

A server that helps people access and query data in databases using the Query Runner with integration of the Model Context Protocol (MCP) Python SDK. Support databases including PostgreSQL Redshift MySQL Microsoft SQL Server Google APIs Amazon Web Services (via boto3) CockroachDB SQLite

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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

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

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

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:






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

Resource Description
schema://all Get the complete database schema

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

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

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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