Magg: The MCP Aggregator Details
Magg is an MCP Aggregator – a meta-MCP server that manages, aggregates, and proxies multiple MCP servers. It acts as a central hub for discovering, configuring, and orchestrating MCP servers, allowing large language models to extend their capabilities at runtime. Magg exposes a suite of tools to search, add, configure, enable/disable, and proxy MCP servers and their tools, merging them under unified prefixes and persisting configurations across sessions. It also includes built-in health and status tools, Real-time Notifications, and MBro (MCP Browser) for interactive exploration, making it easier to compose, manage, and monitor complex MCP ecosystems. Whether you’re running stdio, HTTP, or hybrid transports, Magg provides flexible deployment modes, kit management, and secure access with optional JWT-based authentication.
Use Case
Magg serves as a central MCP registry and proxy layer that lets LLMs dynamically discover, configure, and aggregate MCP servers and their tools. Use Magg to: 1) search for MCP servers and fetch setup instructions; 2) add and configure new MCP servers with per-server prefixes; 3) enable/disable servers on demand; 4) aggregate tools from several servers under a unified namespace; 5) reload configuration from disk and manage kits of servers. The documentation includes concrete usage patterns such as running Magg in different transports, interacting with MBro for exploration, and using Magg’s tools via MCP clients. Example: run Magg in hybrid mode and connect with Claude Code or mbro, then list servers or tools via the MCP interface. See code samples below for authenticating and listing tools with MaggClient or FastMCP Client, and for adding a server via Claude Desktop integration.
Available Tools (16)
Examples & Tutorials
Running Magg and getting started:
<h1 class="text-2xl font-semibold mt-5 mb-3">Install Magg as a tool</h1>
uv tool install magg<h1 class="text-2xl font-semibold mt-5 mb-3">Run with stdio transport (Claude Desktop, Cline, etc.)</h1>
magg serve
<h1 class="text-2xl font-semibold mt-5 mb-3">Run with HTTP transport (system-wide access)</h1>
magg serve --http
Alternative: Run directly from GitHub
<h1 class="text-2xl font-semibold mt-5 mb-3">Run with stdio transport</h1>
uvx --from git+https://github.com/sitbon/magg.git magg<h1 class="text-2xl font-semibold mt-5 mb-3">Run with HTTP transport</h1>
uvx --from git+https://github.com/sitbon/magg.git magg serve --http
Local development:
<h1 class="text-2xl font-semibold mt-5 mb-3">Clone the repository</h1>
git clone https://github.com/sitbon/magg.git
cd magg<h1 class="text-2xl font-semibold mt-5 mb-3">Install in development mode with dev dependencies</h1>
uv sync --dev
<h1 class="text-2xl font-semibold mt-5 mb-3">or with poetry</h1>
poetry install --with dev
<h1 class="text-2xl font-semibold mt-5 mb-3">Run the CLI</h1>
magg --help
Claude Desktop / mbro integration examples:
<h1 class="text-2xl font-semibold mt-5 mb-3">Claude Code example to use Magg in hybrid mode</h1>
claude mcp add magg -- magg serve --hybrid --port 42000MBro usage to inspect Magg:
mbro connect magg "magg serve --hybrid --port 8080"
mbro:local-magg> call magg_status
mbro:local-magg> call magg_list_serversMagg client usage (authenticated via JWT):
from magg.client import MaggClientasync def main():
async with MaggClient("http://localhost:8000/mcp") as client:
tools = await client.list_tools()
JWT authentication with FastMCP:
from fastmcp import Client
from fastmcp.client import BearerAuthjwt_token = "your-jwt-token-here"
async with Client("http://localhost:8000/mcp", auth=BearerAuth(jwt_token)) as client:
tools = await client.list_tools()
Installation Guide
Step-by-step installation and setup from the documentation:
1) Quick Install (Recommended):
<h1 class="text-2xl font-semibold mt-5 mb-3">Install Magg as a tool</h1>
uv tool install magg<h1 class="text-2xl font-semibold mt-5 mb-3">Run with stdio transport</h1>
magg serve
<h1 class="text-2xl font-semibold mt-5 mb-3">Run with HTTP transport</h1>
magg serve --http
2) Alternative: Run Directly from GitHub
<h1 class="text-2xl font-semibold mt-5 mb-3">Run with stdio transport</h1>
uvx --from git+https://github.com/sitbon/magg.git magg<h1 class="text-2xl font-semibold mt-5 mb-3">Run with HTTP transport</h1>
uvx --from git+https://github.com/sitbon/magg.git magg serve --http
3) Local Development:
<h1 class="text-2xl font-semibold mt-5 mb-3">Clone the repository</h1>
git clone https://github.com/sitbon/magg.git
cd magg<h1 class="text-2xl font-semibold mt-5 mb-3">Install in development mode with dev dependencies</h1>
uv sync --dev
<h1 class="text-2xl font-semibold mt-5 mb-3">Or with poetry</h1>
poetry install --with dev
<h1 class="text-2xl font-semibold mt-5 mb-3">Run the CLI</h1>
magg --help
Integration Guides
Frequently Asked Questions
Is this your MCP?
Claim ownership and get verified badge
Sponsored
Notes and important details from the docs:
Prerequisites: Python 3.12 or higher (3.13+ recommended); Python package uv is recommended and can be installed from astral.sh/uv. Magg is run as a tool via uv (uv tool install magg).
Compare Alternatives
Similar MCP Tools
9 related toolsPlaywright MCP
Playwright MCP server. A Model Context Protocol (MCP) server that provides browser automation capabilities using Playwright. This server enables large language models (LLMs) to interact with web pages through structured accessibility snapshots, bypassing the need for screenshots or visually-tuned models. The server is designed to be fast, lightweight, and deterministic, offering LLM-friendly tooling and a rich set of browser automation capabilities via MCP tools. It supports standalone operation, containerized deployments, and integration with a variety of MCP clients (Claude Desktop, VS Code, Copilot, Cursor, Goose, Windsurf, and others).
Sequential Thinking MCP Server
Sequential Thinking MCP Server provides a dedicated MCP tool that guides problem-solving through a structured, step-by-step thinking process. It supports dynamic adjustment of the number of thoughts and allows revision and branching within a controlled workflow, making it ideal for complex analysis and solution hypothesis development. This server is designed to register a single tool, sequential_thinking, and is integrated with common MCP deployment methods (NPX, Docker) as well as editor integrations like Claude Desktop and VS Code for quick setup. The documentation provides exact configuration snippets, usage patterns, and building instructions to help you deploy and use the MCP server effectively, including Codex CLI, NPX, and Docker installation examples.
N8N MCP Server
An MCP (Model Context Protocol) server designed to integrate Claude Desktop, Claude Code, Windsurf, and Cursor with n8n workflows. This MCP enables users to build, test, and orchestrate complex workflows by exposing a set of tools that bridge Claude’s capabilities with n8n’s automation platform. The project emphasizes robust trigger handling, multi-tenant readiness, and progressive documentation to help developers understand how tools map to real-world workflow tasks. It also outlines future tooling integration points (such as getNodeEssentials and getNodeInfo) to further enhance node-structure awareness within MCP-powered automations.
Hugging Face MCP Server
Hugging Face Official MCP Server connects your large language models (LLMs) to the Hugging Face Hub and thousands of Gradio AI Applications, enabling seamless MCP (Model Context Protocol) integration across multiple transports. It supports STDIO, SSE (to be deprecated but still commonly deployed), StreamableHTTP, and StreamableHTTPJson, with the Web Application allowing dynamic tool management and status updates. This MCP server is designed to be run locally or in Docker, and it provides integrations with Claude Desktop, Claude Code, Gemini CLI (and its extension), VSCode, and Cursor, making it easy to configure and manage MCP-enabled tools and endpoints. Tools such as hf_doc_search and hf_doc_fetch can be enabled to enhance document discovery, and an optional Authenticate tool can be included to handle OAuth challenges when called.
Shadcn UI MCP Server v4
Shadcn UI v4 MCP Server is an advanced MCP (Model Context Protocol) server designed to give AI assistants comprehensive access to shadcn/ui v4 components, blocks, demos, and metadata. It enables multi-framework support (React, Svelte, Vue, and React Native) with fast, cache-friendly access to component source code, demos, and directory structures, empowering AI-driven development workflows. The project emphasizes production-readiness with Docker Compose, SSE transport for multi-client deployments, and smart caching to optimize GitHub API usage while providing rich metadata and usage patterns for rapid prototyping and learning across frameworks.
Figma MCP server
The Figma MCP server enables design context delivery from Figma files to AI agents and code editors, empowering teams to generate code directly from design selections. It supports both a remote hosted server and a locally hosted desktop server, allowing seamless integration with popular editors through Code Connect and a suite of tools that extract design context, metadata, variables, and more. This guide covers enabling the MCP server, configuring clients (VS Code, Cursor, Claude Code, and others), and using a curated set of MCP tools to fetch structured design data for faster, more accurate code generation. It also explains best practices, prompts, and integration workflows that help teams align generated output with their design systems. The documentation includes concrete JSON examples for configuring servers in editors like VS Code and Cursor, as well as command examples for Claude Code integration and plugin installation.
MarkItDown MCP
MarkItDown-MCP is a lightweight MCP (Model Context Protocol) server provided as the markitdown-mcp package. It exposes a STDIO, Streamable HTTP, and SSE MCP server designed for calling MarkItDown to convert content to Markdown. The package focuses on simplicity and accessibility, enabling you to run the MCP server locally via a simple CLI, or in Docker for containerized workflows, with integration options for Claude Desktop. The core capability is exposed through a single tool, convert_to_markdown(uri), which accepts a URI in http:, https:, file:, or data: schemes to fetch content and convert it to Markdown. This MCP server is easy to install with pip and can be used in various transport modes, including STDIO and HTTP/SSE, making it a flexible choice for automations and integrations.
Chrome MCP Server
Chrome MCP Server is a Chrome extension-based Model Context Protocol (MCP) server that exposes your Chrome browser functionality to AI assistants like Claude, enabling complex browser automation, content analysis, and semantic search. It leverages your existing Chrome environment, including login states and configurations, to allow large language models and chatbots to control the browser natively without needing to launch a separate automation process. The project emphasizes privacy by remaining fully local and offers capabilities such as cross-tab context, streamable HTTP communication, and a built-in vector database for semantic search and content analysis. As an early-stage project, it includes a growing set of tools for browser control, inspection, and automation, with ongoing development to broaden compatibility and features.
MCP server for Appwrite docs
The MCP server for Appwrite docs enables LLMs and code-generation tools to interact with comprehensive Appwrite documentation. It empowers AI assistants to access up-to-date API references, SDK guides, and implementation examples, facilitating intelligent code generation, troubleshooting, and best-practice guidance directly from the official docs. This MCP brings real-time context, semantic search, and seamless integration with popular editors and IDEs to accelerate development workflows around Appwrite's APIs and SDKs.