A streamlined foundation for building Model Context Protocol servers in Python, designed to make AI-assisted development of MCP tools easier and more efficient.
A foundation for building custom local Model Context Protocol (MCP) servers that provide tools accessible to AI assistants like Cursor or Claude Desktop.
A production-ready, transport-independent foundation for building enterprise MCP servers in Python. Provides infrastructure, configuration, DI, logging, and tooling so downstream projects can focus on domain capabilities.
A production-ready foundation for building secure, observable MCP servers with built-in authentication, rate limiting, and reference tools like database-query and semantic-search.
A production-ready foundation for developing custom Model Context Protocol (MCP) servers in TypeScript. Provides a complete layered architecture pattern, working example implementation, and comprehensive developer infrastructure to connect AI assistants with external APIs and data sources.