Hatago MCP Hub
Hatago MCP Hub is a lightweight multi - MCP server aggregation center that supports connecting local, remote, and NPX - packaged MCP servers via multiple transport protocols such as STDIO, HTTP, and SSE, providing a unified access entry for AI tools such as Claude Code and Cursor, and featuring advanced functions such as configuration inheritance, tag filtering, and environment variable expansion.
rating : 2.5 points
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What is Hatago MCP Hub?
Hatago is a transit station that connects modern AI tools with MCP servers. It's like an intelligent router that allows you to configure and manage all MCP servers in one place and then provide them to AI assistants through a unified interface.How to use Hatago MCP Hub?
Using Hatago is very simple: First, create a configuration file to define the MCP servers you want to connect to (such as file systems, GitHub, databases, etc.), and then start the Hatago service. AI assistants only need to connect to Hatago to access the functions of all configured servers.Applicable scenarios
Suitable for developers and teams who need to use multiple AI tools simultaneously, especially those using AI programming tools such as Claude Code, Cursor, and Windsurf. Also suitable for users who need to switch different tool configurations in different projects or environments.Main Features
High-performance startup
The startup speed is up to 8.44 times faster (optimized from 85.66ms to 10.14ms), and the package size is reduced by 17% (from 1.04MB to 854KB).
Multi-transport protocol support
Supports three connection methods: STDIO, HTTP, and SSE, and is compatible with various AI tools such as Claude Code, Codex CLI, and Cursor.
Tag filtering function
You can add tags to servers (such as 'dev', 'production') and only load servers with specific tags during startup to manage tool combinations flexibly.
Configuration inheritance
Supports configuration file inheritance. You can create a base configuration and specific environment configurations to avoid duplicate settings and follow the DRY principle.
Remote server proxy
You can connect to remote HTTP/SSE MCP servers, such as DeepWiki MCP, to expand the scope of tool capabilities.
Progress notification forwarding
Transparently forwards progress notifications from sub - servers, supports long - running operations, and provides real - time feedback.
Advantages
One - stop management: Manage all MCP servers in a unified manner and simplify configuration
Flexible deployment: Supports local, remote, and NPX servers to adapt to various scenarios
Excellent performance: Fast startup speed and low resource consumption
Easy to use: Start the HTTP mode with zero configuration, suitable for quick start
Team - friendly: Supports configuration inheritance and tag filtering for easy team collaboration
Limitations
Restart required for configuration changes: Manually restart the service after modifying the configuration file
Learning curve: Need to understand the MCP protocol and server configuration
Dependent on external tools: Some functions (such as automatic restart) need to be used in conjunction with nodemon or PM2
How to Use
Install Hatago
You can install it globally via npm or run it directly using npx without installation.
Create a configuration file
Use the init command to create a basic configuration file or manually create hatago.config.json.
Configure MCP servers
Add the MCP servers you need, such as file systems, GitHub, databases, etc., to the configuration file.
Start the Hatago service
Select the appropriate startup mode (STDIO or HTTP) according to your AI tool.
Configure AI tool connection
Configure the connection to Hatago in your AI tool. The specific method depends on the tool type.
Usage Examples
Development environment toolset
Use the file system, GitHub, and code search tools simultaneously when developing a project.
Team standardization configuration
The team shares the basic configuration, and individuals add specific tools according to their needs.
Multi - environment switching
Use different tool combinations in different environments (development, testing, production).
Frequently Asked Questions
What's the difference between Hatago and directly using MCP servers?
Which AI tools are supported?
Do I need to restart after changing the configuration?
How to debug connection issues?
Does it support custom MCP servers?
Related Resources
Official Documentation
Complete documentation for Hatago MCP Hub (Japanese/English)
GitHub Repository
Source code and issue tracking
npm Package
Installation package and version history
MCP Protocol Specification
Official specification of the Model Context Protocol
Getting Started Tutorial (Dev.to)
Getting started tutorial for Hatago MCP Hub

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