Remote-MCP Server

Overview

Remote‑MCP provides a type‑safe, bidirectional bridge that lets an MCP client—such as Claude or any other AI assistant—talk to a remote MCP server over HTTP. The core problem it solves is the lack of immediate remote access in the official MCP roadmap, which only promises support for remote servers later in 2025. By enabling a local client to reach out to a centrally hosted MCP server, developers can centralise data sources, APIs, and database connections while keeping the client lightweight.

The server exposes all standard MCP capabilities—resources, tools, prompts, and sampling—through a simple tRPC‑based HTTP API. This design keeps the communication protocol familiar to MCP users while adding a thin network layer that can be deployed on Cloudflare Workers, standalone Node.js instances, or any environment that supports HTTP. The architecture decouples the client from local resource handling; the client talks to a lightweight “gateway” that forwards requests to the remote MCP backend, which then interacts with databases or external web APIs.

Key features include:

• Zero‑configuration client: just point the environment variable and add an optional authorization header.
• Type‑safe bindings: the client library generates strongly typed interfaces from the remote server’s schema, preventing runtime errors.
• Bidirectional streaming: the same tRPC channel supports both client‑initiated requests and server‑pushed events, enabling real‑time updates.
• Extensible resource access: any number of databases or APIs can be registered on the remote server, allowing a single client to tap into diverse data ecosystems.

Typical use cases are:

• Enterprise AI assistants that need to query corporate databases or internal services without exposing those resources directly to the client.
• Multi‑tenant SaaS platforms where each tenant runs its own MCP server but shares a common client application.
• Edge deployments (e.g., Cloudflare Workers) that expose MCP endpoints to local AI agents running on user devices.

Integrating Remote‑MCP into an existing workflow is straightforward: replace the local MCP server configuration with a remote URL, and the client continues to operate as before. The remote server handles all resource resolution, leaving the local agent free to focus on prompt generation and user interaction. This separation of concerns simplifies scaling, improves security by centralising credentials, and reduces the attack surface for client‑side code.

In summary, Remote‑MCP delivers immediate, production‑ready remote MCP support with minimal friction. Its type‑safe, bidirectional architecture and lightweight deployment options make it a compelling choice for developers who need to connect AI assistants to centralized data sources without waiting for future official releases.