A reference implementation demonstrating proper MCP server patterns with HTTP transport, featuring session management, progress notifications, and example tools for testing server functionality. Serves as a clean template for building MCP servers with streamable responses and comprehensive error handling.
Durable MCP server for managing long-running jobs locally, over SSH, or on Slurm clusters. Jobs survive client disconnects and return exit codes, bounded logs, and JSON artifacts.
Async MCP server for running long-running AI tasks with real-time progress monitoring, enabling users to start, monitor, and manage complex AI workflows across multiple models.
