MCP Hub

The MCP‑Hub‑MCP server is a lightweight gateway that consolidates multiple Model Context Protocol (MCP) services into a single, easily‑managed endpoint. In many AI‑powered workflows developers end up juggling dozens of specialized MCP servers—each exposing a distinct set of tools for file manipulation, API integration, or test automation. The hub eliminates the overhead of connecting to each server individually by maintaining a configurable list of back‑end MCPs and routing requests on demand. This reduces context pollution, limits the surface area for potential errors, and keeps the active toolset focused on what is actually needed at any given moment.

At its core, the hub offers three intuitive commands that mirror common developer tasks. The command aggregates tool catalogs from every connected server, providing a single view of all available actions. lets the AI client invoke any tool on a specified server by name, passing along arbitrary arguments; this is essentially a remote procedure call that abstracts away the complexities of inter‑process communication. Finally, performs a regex‑based search across all tool names, enabling quick discovery of capabilities without having to consult each server’s documentation separately. Together these operations give developers a declarative way to orchestrate diverse toolchains without scattering configuration across multiple environments.

Because the hub is itself an MCP server, it can be plugged into any existing AI assistant that understands the protocol. Developers simply point their client at the hub’s URL, and the assistant can enumerate tools, select the appropriate one, and pass arguments—all while the hub transparently forwards requests to the correct underlying server. This seamless integration means that the same AI workflow can leverage a file‑system MCP for reading configuration files, an Atlassian MCP for interacting with JIRA, and a Playwright MCP for automated UI tests, all through a unified interface. The result is a cleaner, more maintainable AI pipeline that scales with the number of external services.

The hub’s configuration is deliberately simple yet powerful. A single JSON file lists each back‑end server by name, the command used to start it, and any required arguments or environment variables. This file can be supplied via an environment variable, a command‑line flag, or by placing it in the working directory. The server then spawns each child MCP process and keeps them alive, reconnecting automatically if a connection drops. Because the hub itself is stateless beyond these connections, it can be deployed in containerized environments or as part of a CI/CD pipeline without additional state management.

In summary, MCP‑Hub‑MCP turns a fragmented collection of specialized tools into a cohesive, on‑demand service layer. By centralizing tool discovery, execution, and search behind a single MCP endpoint, it streamlines AI assistant workflows, reduces context noise, and makes it trivial to add or remove capabilities as projects evolve. This architecture is especially valuable in large teams, multi‑project environments, or any scenario where AI assistants must interact with a diverse set of external systems without becoming bloated or error‑prone.