sitbon/magg

[//]: # ([](https://github.com/sitbon/magg/actions/workflows/test.yml))
A *Model Context Protocol* server that manages, aggregates, and proxies other MCP servers, enabling LLMs to dynamically extend their own capabilities.
What is Magg?
Magg is a meta-MCP server that acts as a central hub for managing multiple MCP servers. It provides tools that allow LLMs to:
- Search for new MCP servers and discover setup instructions
- Add and configure MCP servers dynamically
- Enable/disable servers on demand
- Aggregate tools from multiple servers under unified prefixes
- Persist configurations across sessions
Think of Magg as a "package manager for LLM tools" - it lets AI assistants install and manage their own capabilities at runtime.
Features
- **Self-Service Tool Management**: LLMs can search for and add new MCP servers without human intervention.
- **Dynamic Configuration Reloading**: Automatically detects and applies config changes without restarting.
- **Automatic Tool Proxying**: Tools from added servers are automatically exposed with configurable prefixes.
- **ProxyMCP Tool**: A built-in tool that proxies the MCP protocol to itself, for clients that don't support notifications or dynamic tool updates (which is most of them currently).
- **Smart Configuration**: Uses MCP sampling to intelligently configure servers from just a URL.
- **Persistent Configuration**: Maintains server configurations in .magg/config.json.
- **Multiple Transport Support**: Works with stdio, HTTP, and in-memory transports.
- **Bearer Token Authentication**: Optional RSA-based JWT authentication for secure HTTP access.
- **Docker Support**: Pre-built images for production, staging, and development workflows.
- **Health Monitoring**: Built-in magg_status and magg_check tools for server health checks.
- **Real-time Messaging**: Full support for MCP notifications and messages - receive tool/resource updates and progress notifications from backend servers.
- **Python 3.12+ Support**: Fully compatible with Python 3.12 and 3.13.
- **Kit Management**: Bundle related MCP servers into kits for easy loading/unloading as a group.
- **MBro CLI**: Included MCP Browser for interactive exploration and management of MCP servers, with script support for automation.
Installation
Prerequisites
- Python 3.12 or higher (3.13+ recommended)
- uv (recommended) - Install from astral.sh/uv
Quick Install (Recommended)
The easiest way to install Magg is as a tool using uv:
Alternative: Run Directly from GitHub
You can also run Magg directly from GitHub without installing:
Local Development
For development, clone the repository and install in editable mode:
Docker
Magg is available as pre-built Docker images from GitHub Container Registry:
Docker Image Strategy
Magg uses a multi-stage Docker build with three target stages:
- **pro (Production)**: Minimal image with WARNING log level, suitable for production deployments
- **pre (Pre-production)**: Same as production but with INFO log level for staging/testing (available but not published)
- **dev (Development)**: Includes development dependencies and DEBUG logging for troubleshooting
Images are automatically published to GitHub Container Registry with the following tags:
- **Version tags** (from main branch): 1.2.3, 1.2, dev, 1.2-dev, 1.2-dev-py3.12, etc.
- **Branch tags** (from beta branch): beta, beta-dev
- **Python-specific dev tags**: beta-dev-py3.12, beta-dev-py3.13, etc.
Pull requests build and test images but do not publish them unless a maintainer adds the
push-image label, which publishes ephemeral pr-NN / pr-NN-dev tags (same-repo PRs
only). Ephemeral pr-* tags and untagged manifests are cleaned up weekly; version tags