Vercel TypeScript MCP Server 🚀

A production-ready Model Context Protocol (MCP) TypeScript Server designed specifically for Vercel Serverless Functions using the Next.js App Router and mcp-handler.

🌟 Why MCP on Vercel?

Traditional MCP servers communicate via stdio (standard input/output for local processes) or long-lived Server-Sent Events (SSE).

On serverless architectures like Vercel, Streamable HTTP Transport provides:

• Zero persistent server costs: Spins up and down per-request with sub-millisecond cold starts.

• Global Edge / Serverless deployment: Scalable across worldwide regions with automatic TLS termination.

• Universal compatibility: Connect from Claude Desktop, Cursor AI, Windsurf, Vercel AI SDK, or any custom MCP client.

Related MCP server: Vercel MCP

📦 Features & Capabilities

🛠️ Built-in Tools

• calculate: High-precision arithmetic and scientific operations (add, subtract, multiply, divide, power, sqrt).

• text_analyzer: Computes word counts, character density, sentence structures, and estimated reading time.

• time_utility: Returns exact UTC timestamps, epoch time, and converts across any IANA timezone.

📄 Resources

• system://info: Dynamic server specifications, runtime environment, and operational health.

💬 Prompts

• code_review: Comprehensive prompt template for automated multi-language code audits and refactoring recommendations.

🏗️ Architecture

               ┌───────────────────────────────┐
               │    AI Host / Client           │
               │ (Claude Desktop, Cursor, etc) │
               └───────────────┬───────────────┘
                               │
                HTTP POST / GET (Streamable HTTP)
                               │
               ┌───────────────▼───────────────┐
               │     Vercel Edge / Serverless  │
               │   Route: /api/mcp/route.ts    │
               │                               │
               │   ┌────────────────────────┐  │
               │   │      mcp-handler       │  │
               │   └───────────┬────────────┘  │
               │               │               │
               │   ┌───────────▼────────────┐  │
               │   │ @modelcontextprotocol  │  │
               │   │      TypeScript SDK    │  │
               │   └────────────────────────┘  │
               └───────────────────────────────┘

🚀 Quick Start

1. Clone & Install

git clone https://github.com/Th3NerdSage/vercel-mcp-typescript-server.git
cd vercel-mcp-typescript-server
npm install

2. Run Locally

npm run dev

Your MCP server is now live at http://localhost:3000/api/mcp and the dashboard at http://localhost:3000.

3. Test with the Built-in Test Client

npm run test:client

⚡ Deploy to Vercel

Option A: One-Click Vercel CLI

npm install -g vercel
vercel

Option B: Vercel Dashboard

1. Go to vercel.com/new.

2. Import Th3NerdSage/vercel-mcp-typescript-server.

3. Click Deploy.

Your production endpoint will be: https://<your-project>.vercel.app/api/mcp

🔌 Connecting with AI Clients

1. Claude Desktop (claude_desktop_config.json)

To connect Claude Desktop to your remote Vercel MCP server, use mcp-remote:

{
  "mcpServers": {
    "vercel-mcp": {
      "command": "npx",
      "args": [
        "-y",
        "mcp-remote",
        "https://<your-project>.vercel.app/api/mcp"
      ]
    }
  }
}

2. Cursor AI / Windsurf / VS Code

In Cursor:

1. Go to Settings -> Features -> MCP.

2. Click + Add New MCP Server.

3. Set:

   • Type: command

   • Command: npx -y mcp-remote https://<your-project>.vercel.app/api/mcp

3. Vercel AI SDK Integration

import { StreamableHTTPClientTransport } from "@modelcontextprotocol/sdk/client/streamableHttp.js";
import { experimental_createMcpTools } from "@ai-sdk/mcp";

const transport = new StreamableHTTPClientTransport(
  new URL("https://<your-project>.vercel.app/api/mcp")
);

const mcpTools = await experimental_createMcpTools({ transport });

🛠️ Adding New Tools, Resources & Prompts

Edit app/api/mcp/route.ts:

// Register a new Tool
server.registerTool(
  "my_custom_tool",
  {
    title: "My Custom Tool",
    description: "Brief explanation of what this tool does",
    inputSchema: {
      query: z.string().describe("Search query parameter"),
    },
  },
  async ({ query }) => {
    // Your business logic / DB call / API call here
    return {
      content: [
        {
          type: "text",
          text: `Processed query: ${query}`,
        },
      ],
    };
  }
);

📄 License

MIT © Sadman Asad Samir (Th3NerdSage)