runekaagaard/mcp-alchemy
by Various
A MCP (model context protocol) server that gives the LLM access to and knowledge about relational databases like SQLite, Postgresql, MySQL & MariaDB, Oracle, and MS-SQL.
MCP
runekaagaard/mcp-alchemy
Added 1 June 2026
Overview
runekaagaard/mcp-alchemy is a Python-based Model Context Protocol (MCP) server that provides LLMs with access to relational databases. It supports SQLite, PostgreSQL, MySQL, MariaDB, Oracle, and MS-SQL, allowing language models to query and interact with database schemas and data.
Best for
Best for
Developers needing to integrate LLMs with existing relational databases in a standardized way
Use cases
- Query databases using natural language from an MCP-compatible LLM client
- Expose database schema to AI agents for automated analysis
- Enable data retrieval and exploration in MCP-driven applications
How to use
Install
uv pip install psycopg2-binary Tools exposed
all_table_namesfilter_table_namesschema_definitionsexecute_query
Tested with
Claude Desktop
Notes
runekaagaard/mcp-alchemy is a Python-based Model Context Protocol (MCP) server that provides LLMs with access to relational databases. It supports SQLite, PostgreSQL, MySQL, MariaDB, Oracle, and MS-SQL, allowing language models to query and interact with database schemas and data.
405 stars on GitHub. Last updated 2025-08-15. Licensed MPL-2.0.
Use cases
- Query databases using natural language from an MCP-compatible LLM client
- Expose database schema to AI agents for automated analysis
- Enable data retrieval and exploration in MCP-driven applications
Pros
- Supports a wide range of databases (SQLite, PostgreSQL, MySQL, MariaDB, Oracle, MS-SQL)
- Built on the MCP standard for interoperability with various LLM clients
- Written in Python, making it easy to extend or deploy in existing workflows
Cons
- Requires a compatible MCP client to function
- May expose sensitive database information if access controls are not properly configured
- Performance is constrained by database size and query complexity
Indexed from awesome-mcp-servers-punkpeye and enriched against its public facts.
Pros
- Supports a wide range of databases (SQLite, PostgreSQL, MySQL, MariaDB, Oracle, MS-SQL)
- Built on the MCP standard for interoperability with various LLM clients
- Written in Python, making it easy to extend or deploy in existing workflows
Cons
- Requires a compatible MCP client to function
- May expose sensitive database information if access controls are not properly configured
- Performance is constrained by database size and query complexity
Pairs with
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