MCP Alchemy

Status: Actively maintained and in daily use. Tested releases are published to PyPI with git tags, and issues and pull requests are triaged regularly.

Let Claude be your database expert! MCP Alchemy connects Claude Desktop directly to your databases, allowing it to:

• Help you explore and understand your database structure
• Assist in writing and validating SQL queries
• Displays relationships between tables
• Analyze large datasets and create reports
• Claude Desktop Can analyse and create artifacts for very large datasets using claude-local-files.

Works with PostgreSQL, MySQL, MariaDB, SQLite, Oracle, MS SQL Server, CrateDB, Vertica, and a host of 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

Usage with Claude Desktop

Add to your claude_desktop_config.json. You need to add the appropriate database driver in the `--with` parameter.

_Note: After a new version release there might be a period of up to 600 seconds while the cache clears locally cached causing uv to raise a versioning error. Restarting the MCP client once again solves the error._

SQLite (built into Python)

{
  "mcpServers": {
    "my_sqlite_db": {
      "command": "uvx",
      "args": ["--from", "mcp-alchemy==2026.10.6.103105",
               "--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==2026.10.6.103105", "--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==2026.10.6.103105", "--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==2026.10.6.103105", "--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==2026.10.6.103105", "--with", "oracledb",
               "--refresh-package", "mcp-alchemy", "mcp-alchemy"],
      "env": {
        "DB_URL": "oracle+oracledb://user:password@localhost/dbname"
      }
    }
  }
}

CrateDB

{
  "mcpServers": {
    "my_cratedb": {
      "command": "uvx",
      "args": ["--from", "mcp-alchemy==2026.10.6.103105", "--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:password@example.aks1.westeurope.azure.cratedb.net:4200?ssl=true.

Vertica

{
  "mcpServers": {
    "my_vertica_db": {
      "command": "uvx",
      "args": ["--from", "mcp-alchemy==2026.10.6.103105", "--with", "vertica-python",
               "--refresh-package", "mcp-alchemy", "mcp-alchemy"],
      "env": {
        "DB_URL": "vertica+vertica_python://user:password@localhost:5433/dbname",
        "DB_ENGINE_OPTIONS": "{\"connect_args\": {\"ssl\": false}}"
      }
    }
  }
}

Docker

A container image is published to GitHub Container Registry with common database drivers (PostgreSQL, MySQL/MariaDB, MS SQL Server, Oracle) preinstalled:

{
  "mcpServers": {
    "my_db": {
      "command": "docker",
      "args": ["run", "-i", "--rm", "-e", "DB_URL",
               "ghcr.io/runekaagaard/mcp-alchemy:latest"],
      "env": {
        "DB_URL": "postgresql://user:password@host.docker.internal/dbname"
      }
    }
  }
}

Or build it yourself with docker build -t ghcr.io/runekaagaard/mcp-alchemy .

Transports

By default the server speaks stdio. It can also serve over HTTP for clients that connect that way:

# Recommended HTTP transport (serves on http://HOST:PORT/mcp)
mcp-alchemy --transport streamable-http --host 127.0.0.1 --port 7000

# Legacy SSE transport, for older clients (serves on http://HOST:PORT/sse)
mcp-alchemy --transport sse --host 127.0.0.1 --port 7000

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)
• DB_ENGINE_OPTIONS: JSON string containing additional SQLAlchemy engine options (optional)

Connection Pooling

MCP Alchemy uses connection pooling optimized for long-running MCP servers. The default settings are:

• pool_pre_ping=True: Tests connections before use to handle database timeouts and network issues
• pool_size=1: Maintains 1 persistent connection (MCP servers typically handle one request at a time)
• max_overflow=2: Allows up to 2 additional connections for burst capacity
• pool_recycle=3600: Refreshes connections older than 1 hour (prevents timeout issues)
• isolation_level='AUTOCOMMIT': Ensures each query commits automatically

These defaults work well for most databases, but you can override them via DB_ENGINE_OPTIONS:

{
  "DB_ENGINE_OPTIONS": "{\"pool_size\": 5, \"max_overflow\": 10, \"pool_recycle\": 1800}"
}

For databases with aggressive timeout settings (like MySQL's 8-hour default), the combination of pool_pre_ping and pool_recycle ensures reliable connections.

API

Tools

• all_table_names
• Return all table names in the database
• No input required
• Returns comma-separated list of tables

  users, orders, products, categories

• filter_table_names
• Find tables matching a substring
• Input: q (string)
• Returns matching table names

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

• schema_definitions
• Get detailed schema for specified tables
• Input: table_names (string[])
• Returns table definitions including:
• Column names and types
• Primary keys
• Foreign key relationships
• Nullable flags

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

• execute_query
• Execute 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

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.

Developing

First clone the github repository, install the dependencies and your database driver(s) of choice:

git clone git@github.com:runekaagaard/mcp-alchemy.git
cd mcp-alchemy
uv sync
uv pip install psycopg2-binary

Then set this in claude_desktop_config.json:

...
"command": "uv",
"args": ["run", "--directory", "/path/to/mcp-alchemy", "-m", "mcp_alchemy.server", "main"],
...

My Other LLM Projects

• MCP Redmine - Let Claude Desktop manage your Redmine projects and issues.
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