Hey! I just launched YAMCP (pronounced “YAM-C-P”), an open-source CLI that bundles any number of Model Context Protocol (MCP) servers, local or remote, into unified local workspaces for your AI apps.
The problem yamcp intends to solve: Juggling multiple MCP servers across different AI tools means editing different config files and digging through separate log files whenever something breaks.
What it offers: YAMCP lets you create dedicated “YAM” workspaces organized however works best for your work styles, by function (e.g. coding, design, research), by app (Cursor, Claude, GitHub-Copilot), or any custom combination in between and expose them as Yet-Another-MCP. Scan an entire YAM workspace for issues, connect your AI apps to a single gateway, and manage everything from one simple gateway.
Why YAMCP?
* Organize your servers locally: Group MCP server by AI application or workflow purpose.
* Scan and Catch errors early: Scan workspaces for misconfigurations before they disrupt your work.
* Single gateway per each workspace: Point all your AI tools at one MCP instead of constantly swapping servers and manage your servers through a single cli environment locally.
* Unified management Add, remove, and update servers with a cli command.
* Centralized logging to debug all servers from one place. Track every request and response across all your MCP connections in one place.
Check out the repo for install steps and examples: https://github.com/hamidra/yamcp
Planning to add server evaluations and an initial security scan to score MCP servers’ reliability and performance.
Would love for you to try it and share what features you’d like to see next!
