Railway Mcp Servers

One-click deploy of a Model Context Protocol (MCP) server gateway on Railway. Exposes multiple MCP tool servers over HTTP using SSE and Streamable HTTP transports, so any MCP-compatible AI client can connect remotely.

Deploy on Railway

What is MCP?

The Model Context Protocol is an open standard for connecting AI assistants to external tools and data sources. MCP servers expose tools (web fetching, memory, reasoning) that AI clients like Claude Desktop, Cursor, Windsurf, and others can use.

This template deploys a gateway that bundles multiple MCP servers behind a single HTTP endpoint, making them accessible over the network instead of requiring local stdio connections.

Included MCP Servers

Server	Path	Description
Fetch	/servers/fetch/sse	Fetches web content and converts it to clean markdown for LLM consumption
Memory	/servers/memory/sse	Persistent knowledge graph for storing and retrieving structured information
Sequential Thinking	/servers/sequential-thinking/sse	Dynamic problem-solving through reflective thought sequences

Environment Variables

Variable	Description	Default
PORT	HTTP port for the gateway	8080
MCP_TRANSPORT	Transport protocol (streamablehttp or sse)	streamablehttp
MCP_CORS_ORIGIN	CORS allowed origin	*
MCP_CONFIG_FILE	Path to custom server config JSON	/default-servers.json

Connecting Clients

Claude Desktop / Cursor / Windsurf

Add this to your MCP client configuration. Uses supergateway or mcp-proxy locally to bridge the remote SSE server to local stdio:

{
  "mcpServers": {
    "remote-fetch": {
      "command": "npx",
      "args": [
        "-y", "supergateway",
        "--sse", "https://YOUR_RAILWAY_DOMAIN/servers/fetch/sse"
      ]
    },
    "remote-memory": {
      "command": "npx",
      "args": [
        "-y", "supergateway",
        "--sse", "https://YOUR_RAILWAY_DOMAIN/servers/memory/sse"
      ]
    },
    "remote-thinking": {
      "command": "npx",
      "args": [
        "-y", "supergateway",
        "--sse", "https://YOUR_RAILWAY_DOMAIN/servers/sequential-thinking/sse"
      ]
    }
  }
}

Replace YOUR_RAILWAY_DOMAIN with your Railway public domain.

Direct HTTP (Streamable HTTP)

For clients that support Streamable HTTP natively, connect to:

https://YOUR_RAILWAY_DOMAIN/servers/fetch/mcp
https://YOUR_RAILWAY_DOMAIN/servers/memory/mcp
https://YOUR_RAILWAY_DOMAIN/servers/sequential-thinking/mcp

Status Check

GET https://YOUR_RAILWAY_DOMAIN/status

Custom Server Configuration

To add or remove MCP servers, mount a custom servers.json file or set MCP_CONFIG_FILE to point to your own config. The format:

{
  "mcpServers": {
    "server-name": {
      "command": "command-to-run",
      "args": ["arg1", "arg2"],
      "env": {
        "KEY": "value"
      }
    }
  }
}

Each server is accessible at /servers/<server-name>/sse (SSE) or /servers/<server-name>/mcp (Streamable HTTP).

Volume

Data is persisted at /data. The memory server stores its knowledge graph at /data/memory/memory.json, ensuring data survives redeployments.

Architecture

Client (Claude Desktop, Cursor, etc.)
  |
  | HTTPS (SSE or Streamable HTTP)
  v
MCP Proxy Gateway (this template)
  |
  |-- /servers/fetch/             -> mcp-server-fetch (stdio)
  |-- /servers/memory/            -> mcp-server-memory (stdio)
  |-- /servers/sequential-thinking/ -> mcp-server-sequential-thinking (stdio)

The gateway uses mcp-proxy to bridge stdio-based MCP servers to HTTP transports, so each server runs as a child process and communicates over stdin/stdout internally.

Links

• Model Context Protocol Specification
• Official MCP Servers
• mcp-proxy
• supergateway (client-side bridge)