MCP Alchemy

What is MCP-Alchemy? MCP-Alchemy is a Python server implementing the Model Context Protocol (MCP) for SQL database operations, designed to give Machine Learning Models access to relational databases like SQLite, PostgreSQL, MySQL, MariaDB, Oracle, and MS-SQL. How to use MCP-Alchemy? To use MCP-Alchemy, clone the repository, set up the appropriate database connection details in the environment variables, and run the server using the provided command structure in your configuration file. Key features of MCP-Alchemy? Execute SQL queries with readable vertical output format. Introspect database schemas and column relationships. List and filter tables in the database. Handle large result sets with smart truncation. Allow full result access via Claude Desktop artifacts. Clean handling of NULL values and date formats. Use cases of MCP-Alchemy? Integrating LLMs with SQL databases for enhanced data retrieval. Simplifying SQL query execution and output formatting for developers. Facilitating database introspection and schema management for data engineers. FAQ from MCP-Alchemy? What databases are supported? MCP-Alchemy supports any SQLAlchemy compatible database, including MySQL, PostgreSQL, SQLite, Oracle, and MS-SQL. Do I need to install anything to run MCP-Alchemy? Yes, you need to set up your database connection details via environment variables as outlined in the documentation before running the server. Is there a limit on the query length supported by MCP-Alchemy? Yes, the default maximum output length is 4000 characters, but this can be increased by setting the EXECUTE_QUERY_MAX_CHARS environment variable.

As an MCP (Model Context Protocol) server, MCP Alchemy enables AI agents to communicate effectively through standardized interfaces. The Model Context Protocol simplifies integration between different AI models and agent systems.

To use MCP-Alchemy, clone the repository, set up the appropriate database connection details in the environment variables, and run the server using the provided command structure in your configuration file. Key features of MCP-Alchemy? Execute SQL queries with readable vertical output format. Introspect database schemas and column relationships. List and filter tables in the database. Handle large result sets with smart truncation. Allow full result access via Claude Desktop artifacts. Clean handling of NULL values and date formats. Use cases of MCP-Alchemy? Integrating LLMs with SQL databases for enhanced data retrieval. Simplifying SQL query execution and output formatting for developers. Facilitating database introspection and schema management for data engineers. FAQ from MCP-Alchemy? What databases are supported? MCP-Alchemy supports any SQLAlchemy compatible database, including MySQL, PostgreSQL, SQLite, Oracle, and MS-SQL. Do I need to install anything to run MCP-Alchemy? Yes, you need to set up your database connection details via environment variables as outlined in the documentation before running the server. Is there a limit on the query length supported by MCP-Alchemy? Yes, the default maximum output length is 4000 characters, but this can be increased by setting the EXECUTE_QUERY_MAX_CHARS environment variable.

Learn how to integrate this MCP server with your AI agents and leverage the Model Context Protocol for enhanced capabilities.