MCP Server Hub

MCP Server Hub / Gateway – Unified Tool Access for AI Assistants

The MCP Server Hub solves a common pain point in modern LLM workflows: each large‑language‑model client (Cline, Cursor, Claude Desktop, etc.) typically requires its own dedicated MCP server to expose tools and data sources. Managing multiple server processes, keeping them in sync, and updating their configurations becomes tedious and error‑prone. The Hub centralises this complexity by running a single persistent gateway that orchestrates all underlying MCP servers and hub‑native tools. Developers can now point every LLM client at one endpoint, eliminating duplicate processes and simplifying deployment.

At its core the Hub consists of two cooperating components. The Gateway Server (the hub process) loads an initial configuration file and then watches that same file for changes. Whenever the config is modified, it automatically restarts or updates any managed MCP servers listed under , reloads hub tools from , and refreshes internal services. It exposes every tool with a clear namespace ( for managed servers, for hub tools) and routes incoming tool calls from clients to the correct backend. The Gateway Client is a lightweight proxy that LLM applications connect to via STDIO; it forwards MCP requests over WebSocket, periodically polls for updated tool lists, and reconnects automatically if the gateway goes down. Together they provide a seamless, single‑entry interface for all tool interactions.

Key capabilities include:

• Dynamic configuration reloading: no server restarts needed when adding or removing MCP servers or tools.
• Namespace‑based tool exposure: prevents name collisions and makes it obvious which server a tool originates from.
• Internal services: custom logic (e.g., ) can react to configuration changes and expose new toolsets on the fly.
• WebSocket‑based communication: low‑latency, bidirectional routing between clients and backend servers.
• Polling for tool updates: ensures LLM clients always see the latest available tools without manual intervention.

Typical use cases span from enterprise environments that maintain several specialized MCP servers (e.g., a database connector, a web‑scraping tool, and a proprietary analytics engine) to hobbyists running multiple lightweight servers locally. By funneling all tool calls through the hub, developers can write a single client configuration and avoid the clutter of multiple port assignments or environment variables. The Hub also simplifies scaling: new servers can be added to the config and immediately become available to all connected LLM clients, making it an ideal backbone for collaborative AI platforms or multi‑tenant services.