MCPM - Model Context Protocol Manager

Overview

MCPM is a lightweight command‑line utility designed to simplify the lifecycle management of Model Context Protocol (MCP) servers on a local machine. In many AI development environments, multiple MCP instances may be required—each exposing different data sources or toolsets to an assistant. Managing these servers manually can become tedious, especially when keeping track of which instance is active or ensuring that outdated versions are removed. MCPM addresses this pain point by providing a single, consistent interface for common administrative tasks: listing available MCPs, inspecting the currently running instance, stopping a server, pulling (retrieving) new servers from a registry, and deleting obsolete ones.

The core value proposition lies in its role as an orchestrator. Developers no longer need to juggle separate docker commands, virtual‑environment activations, or manual file manipulations. Instead, they issue a simple command with subcommands that handle the underlying operations behind the scenes. This streamlines workflows where an AI assistant must switch between different MCP configurations—such as toggling from a local knowledge base to a remote data source—or when testing new server builds in isolation. By abstracting these operations, MCPM reduces friction and lowers the barrier to experimentation.

Key capabilities include:

• Discovery – surfaces every MCP image or package present on the host, giving developers a quick inventory of available protocols.
• Runtime inspection – reports the active MCP, enabling rapid verification that the correct server is serving requests.
• Graceful shutdown – cleanly terminates the current instance, preventing orphaned processes that could consume resources or interfere with subsequent launches.
• Acquisition – fetches a specified MCP from a remote registry or repository, ensuring that the latest version is ready for use.
• Cleanup – deletes an MCP from the local environment, freeing disk space and preventing accidental deployment of stale protocols.

In real‑world scenarios, MCPM proves invaluable for teams building AI assistants that need to switch contexts rapidly—such as a customer‑support bot toggling between an internal FAQ MCP and an external knowledge graph MCP, or a research assistant cycling through experimental data‑access protocols during development. It also aids continuous integration pipelines where automated tests spin up specific MCPs, verify functionality, and tear them down cleanly.

What sets MCPM apart is its minimal footprint coupled with a consistent, predictable command set that aligns closely with standard UNIX tooling. Developers familiar with CLI patterns can adopt it immediately, while the abstraction layer ensures that underlying complexities—container orchestration, dependency resolution, or network configuration—remain hidden. This focus on developer ergonomics makes MCPM a practical companion for any project that relies on Model Context Protocols to bridge AI assistants with external resources.