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MCP servers/Databases
runekaagaard avatar

MCP Alchemy Server

runekaagaard/mcp-alchemy

MCP Alchemy integrates Claude Desktop directly with multiple database systems including PostgreSQL, MySQL, SQLite, Oracle, MS SQL Server, and other SQLAlchemy-compatible databases, enabling Claude to explore database structures, write and validate SQL queries, visualize table relationships, and analyze datasets. The server provides tools for database exploration, SQL query assistance, relationship mapping, and large dataset analysis with artifact generation capabilities. By connecting Claude to databases as an expert assistant, MCP Alchemy solves the problem of requiring manual database interaction and SQL expertise for data exploration and reporting tasks.

GitHub stars
403
last updated
Mar 9, 2026
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Readme

MCP Alchemy

PulseMCP Badge

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.

MCP Alchemy in action

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==2026.10.15.202659",
               "--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==2026.10.15.202659", "--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==2026.10.15.202659", "--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==2026.10.15.202659", "--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==2026.10.15.202659", "--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==2026.10.15.202659", "--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==2026.10.15.202659", "--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.
  • MCP Notmuch Sendmail - Email assistant for Claude Desktop using notmuch.
  • MCP Show-on-Kindle - Copy a file to a Kindle running KOReader and open it on screen.
  • Diffpilot - Multi-column git diff viewer with file grouping and tagging.
  • Claude Local Files - Access local files in Claude Desktop artifacts.

MCP Directory Listings

MCP Alchemy is listed in the following MCP directory sites and repositories:

  • PulseMCP
  • Glama
  • MCP.so
  • MCP Archive
  • Playbooks MCP
  • Awesome MCP Servers

Contributing

Contributions are warmly welcomed! Whether it's bug reports, feature requests, documentation improvements, or code contributions - all input is valuable. Feel free to:

  • Open an issue to report bugs or suggest features
  • Submit pull requests with improvements
  • Enhance documentation or share your usage examples
  • Ask questions and share your experiences

The goal is to make database interaction with Claude even better, and your insights and contributions help achieve that.

License

Mozilla Public License Version 2.0

Details

Repository
github.com/runekaagaard/mcp-alchemy
Categories
Databases, AI & LLM Tools, Documents & Knowledge

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