SQL Alchemy MCP server

MCP Alchemy connects Claude Desktop directly to your databases, allowing it to help explore database structure, assist with SQL queries, display table relationships, and analyze large datasets. It works with PostgreSQL, MySQL, MariaDB, SQLite, Oracle, MS SQL Server, CrateDB, and other SQLAlchemy-compatible databases.

Installation

Ensure you have uv installed:

# Install uv if you haven't already
curl -LsSf https://astral.sh/uv/install.sh | sh

Configuration for Claude Desktop

Add MCP Alchemy to your claude_desktop_config.json file. You'll need to specify the appropriate database driver in the --with parameter.

SQLite (built into Python)

{
  "mcpServers": {
    "my_sqlite_db": {
      "command": "uvx",
      "args": ["--from", "mcp-alchemy==2025.5.2.210242",
               "--refresh-package", "mcp-alchemy", "mcp-alchemy"],
      "env": {
        "DB_URL": "sqlite:////absolute/path/to/database.db"
      }
    }
  }
}

PostgreSQL

{
  "mcpServers": {
    "my_postgres_db": {
      "command": "uvx",
      "args": ["--from", "mcp-alchemy==2025.5.2.210242", "--with", "psycopg2-binary",
               "--refresh-package", "mcp-alchemy", "mcp-alchemy"],
      "env": {
        "DB_URL": "postgresql://user:password@localhost/dbname"
      }
    }
  }
}

MySQL/MariaDB

{
  "mcpServers": {
    "my_mysql_db": {
      "command": "uvx",
      "args": ["--from", "mcp-alchemy==2025.5.2.210242", "--with", "pymysql",
               "--refresh-package", "mcp-alchemy", "mcp-alchemy"],
      "env": {
        "DB_URL": "mysql+pymysql://user:password@localhost/dbname"
      }
    }
  }
}

Microsoft SQL Server

{
  "mcpServers": {
    "my_mssql_db": {
      "command": "uvx",
      "args": ["--from", "mcp-alchemy==2025.5.2.210242", "--with", "pymssql",
               "--refresh-package", "mcp-alchemy", "mcp-alchemy"],
      "env": {
        "DB_URL": "mssql+pymssql://user:password@localhost/dbname"
      }
    }
  }
}

Oracle

{
  "mcpServers": {
    "my_oracle_db": {
      "command": "uvx",
      "args": ["--from", "mcp-alchemy==2025.5.2.210242", "--with", "cx_oracle",
               "--refresh-package", "mcp-alchemy", "mcp-alchemy"],
      "env": {
        "DB_URL": "oracle+cx_oracle://user:password@localhost/dbname"
      }
    }
  }
}

CrateDB

{
  "mcpServers": {
    "my_cratedb": {
      "command": "uvx",
      "args": ["--from", "mcp-alchemy==2025.5.2.210242", "--with", "sqlalchemy-cratedb>=0.42.0.dev1",
               "--refresh-package", "mcp-alchemy", "mcp-alchemy"],
      "env": {
        "DB_URL": "crate://user:password@localhost:4200/?schema=testdrive"
      }
    }
  }
}

For connecting to CrateDB Cloud, use a URL like crate://user:[email protected]:4200?ssl=true.

Environment Variables

• DB_URL: SQLAlchemy database URL (required)
• CLAUDE_LOCAL_FILES_PATH: Directory for full result sets (optional)
• EXECUTE_QUERY_MAX_CHARS: Maximum output length (optional, default 4000)

Available Tools

all_table_names

Returns all table names in the database as a comma-separated list.

users, orders, products, categories

filter_table_names

Finds tables matching a substring.

Input: q (string) Returns matching table names:

Input: "user"
Returns: "users, user_roles, user_permissions"

schema_definitions

Gets detailed schema for specified tables.

Input: table_names (string[]) Returns table definitions including column details and relationships:

users:
    id: INTEGER, primary key, autoincrement
    email: VARCHAR(255), nullable
    created_at: DATETIME
    
    Relationships:
      id -> orders.user_id

execute_query

Executes SQL query with vertical output format.

Inputs:

• query (string): SQL query
• params (object, optional): Query parameters

Returns results in clean vertical format:

1. row
id: 123
name: John Doe
created_at: 2024-03-15T14:30:00
email: NULL

Result: 1 rows

Features:

• Smart truncation of large results
• Full result set access via claude-local-files integration
• Clean NULL value display
• ISO formatted dates
• Clear row separation

Claude Local Files Integration

When claude-local-files is configured:

• Access complete result sets beyond Claude's context window
• Generate detailed reports and visualizations
• Perform deep analysis on large datasets
• Export results for further processing

The integration automatically activates when CLAUDE_LOCAL_FILES_PATH is set.

How to add this MCP server to Cursor

There are two ways to add an MCP server to Cursor. The most common way is to add the server globally in the ~/.cursor/mcp.json file so that it is available in all of your projects.

If you only need the server in a single project, you can add it to the project instead by creating or adding it to the .cursor/mcp.json file.

Adding an MCP server to Cursor globally

To add a global MCP server go to Cursor Settings > MCP and click "Add new global MCP server".

When you click that button the ~/.cursor/mcp.json file will be opened and you can add your server like this:

{
    "mcpServers": {
        "cursor-rules-mcp": {
            "command": "npx",
            "args": [
                "-y",
                "cursor-rules-mcp"
            ]
        }
    }
}

Adding an MCP server to a project

To add an MCP server to a project you can create a new .cursor/mcp.json file or add it to the existing one. This will look exactly the same as the global MCP server example above.

How to use the MCP server

Once the server is installed, you might need to head back to Settings > MCP and click the refresh button.

The Cursor agent will then be able to see the available tools the added MCP server has available and will call them when it needs to.

You can also explictly ask the agent to use the tool by mentioning the tool name and describing what the function does.