mcpField
V
Vercel MCP Integration

Vercel MCP Integration

Local
github.com/nganietCloud & Infra69Updated 64w ago

About Vercel MCP Integration

Vercel MCP Integration is an MCP server that exposes Vercel REST API endpoints as tools for AI assistants such as Claude and Cursor. It targets developers who want to manage Vercel deployments, projects and teams through an AI assistant.

Key features

  1. 01Deployment listing, lookup and creation
  2. 02Project and environment variable management
  3. 03Custom environment and domain information
  4. 04Team listing and creation

Use cases

  1. 01Asking an AI assistant to list recent production deployments
  2. 02Triggering a new deployment from a Git branch
  3. 03Creating multiple environment variables for a project

MCP support

This project is an MCP server that exposes prefixed tools such as vercel-list-all-deployments to MCP clients.

Configuration

Local server config is the same for every client. Copy it into your client’s MCP config file.

No install method listed yet

We haven’t collected a package or remote endpoint for this server. Follow the instructions in the project README.

Repository

Tools

No tool list yet

Local servers expose tools only once running on your machine, and the maintainer has not provided a tool list.

Details

From the project README

Vercel MCP Integration

A Model Context Protocol (MCP) integration for Vercel's REST API, providing programmatic access to Vercel deployment management through AI Assistants like Claude and Cursor.

📋 Overview Last updated: May 2025

This MCP server implements Vercel's core API endpoints as tools, enabling:

  • Deployment monitoring & management
  • Environment variable retrieval
  • Project deployment status tracking
  • Team creation and management
  • CI/CD pipeline integration

✨ Features

Current Tools
Deployment Management
Project Management
Environment Management
Team Management

🛣️ Roadmap

  • Deployment creation workflow
  • Project management tools
  • Team management integration (List & Create teams)
  • Advanced error handling

📚 Tool Documentation

For detailed information about each tool, please refer to the following documentation:

🚀 Getting Started

Prerequisites
  • Node.js 18+
  • Vercel API Token
  • MCP Client (Claude, Cursor, or other AI Assistants that support MCP)
Installation
git clone [your-repo-url]
cd vercel-mcp
npm install
Configuration
  1. Create .env file:
VERCEL_API_TOKEN=your_api_token_here
  1. Start MCP server:
npm start

🔗 Integrating with AI Assistants

Integrating with Claude

Claude supports MCP tools via its Anthropic Console or Claude Code interface.

  1. Start the MCP server locally with npm start
  2. In Claude Code, use the /connect command:
    /connect mcp --path [path-to-server]
    
    For CLI-based servers using stdio, specify the path to the server executable
  3. Claude will automatically discover the available Vercel tools
  4. You can then ask Claude to perform Vercel operations, for example:
    Please list my recent Vercel deployments using the vercel-list-all-deployments tool
    
  5. Alternatively, you can expose the MCP server as an HTTP server with a tool like mcp-proxy
    npm install -g @modelcontextprotocol/proxy
    mcp-proxy --stdio --cmd "npm start" --port 3399
    
    Then connect in Claude: /connect mcp --url http://localhost:3399
Integrating with Cursor

Cursor has built-in support for MCP tools through its extension system.

  1. Start the MCP server with npm start
  2. In Cursor, access Settings → Tools
  3. Under "Model Context Protocol (MCP)", click "+ Add MCP tool"
  4. Configure a new connection:
    • For stdio transport: Point to the executable path
    • For HTTP transport: Specify the URL (e.g., http://localhost:3399)
  5. Cursor will automatically discover the available Vercel tools
  6. Use Cursor's AI features to interact with your Vercel deployments by mentioning the tools in your prompts
Programmatic Integration

You can also use the Model Context Protocol SDK to integrate with the server programmatically in your own applications:

import { Client } from "@modelcontextprotocol/sdk/client";

// Create an MCP client connected to a stdio transport
const client = new Client({
  transport: "stdio",
  cmd: "npm --prefix /path/to/vercel-mcp start",
});

// Or connect to an HTTP transport
const httpClient = new Client({
  transport: "http",
  url: "http://localhost:3399",
});

// Connect to the server
await client.connect();

// List available tools
const { tools } = await client.listTools();
console.log(
  "Available tools:",
  tools.map((t) => t.name)
);

// Call a tool
const result = await client.callTool({
  name: "vercel-list-all-deployments",
  args: { limit: 5 },
});

console.log("Deployments:", result);

// You can also use this in an Express server:
app.post("/api/deployments", async (req, res) => {
  try {
    const result = await client.callTool({
      name: "vercel-list-all-deployments",
      args: req.body,
    });
    res.json(result);
  } catch (error) {
    res.status(500).json({ error: error.message });
  }
});

🛠️ Tool Usage Examples

Here are some quick examples to get you started. For comprehensive documentation and more examples, please refer to the tool documentation.

List Recent Deployments
const response = await mcpClient.callTool({
  name: "vercel-list-all-deployments",
  args: {
    limit: 5,
    target: "production",
  },
});
Create a New Project
const project = await mcpClient.callTool({
  name: "vercel-create-project",
  args: {
    name: "my-awesome-project",
    framework: "nextjs",
    teamId: "team_1a2b3c4d5e6f7g8h9i0j1k2l",
  },
});
Deploy from Git
const deployment = await mcpClient.callTool({
  name: "vercel-create-deployment",
  args: {
    project: "my-project-id",
    gitSource: {
      type: "github",
      ref: "main",
    },
  },
});

For more detailed examples including file deployments, environment management, and team operations, see the documentation:

🐳 Docker Deployment

Build the Image
docker build -t vercel-mcp .
Run Container
docker run -it --rm \
  -e VERCEL_API_TOKEN=your_token_here \
  -p 3399:3399 \
  vercel-mcp
Production Deployment
docker run -d \
  --name vercel-mcp \
  --restart unless-stopped \
  -e VERCEL_API_TOKEN=your_token_here \
  -p 3399:3399 \
  vercel-mcp
Development with Live Reload
docker build --target builder -t vercel-mcp-dev .
docker run -it --rm \
  -e VERCEL_API_TOKEN=your_token_here \
  -p 3399:3399 \
  -v $(pwd)/src:/app/src \
  vercel-mcp-dev

🗂️ Project Structure

src/
├── constants/       # Tool definitions
├── tools/
│   ├── deployments/ # Deployment handlers
│   │   ├── handlers.ts
│   │   ├── schema.ts
│   │   └── types.ts
│   └── environments/# Environment management
├── utils/          # API helpers
└── index.ts         # Server entrypoint

🔧 Configuration

Environment Variables
VariableDescriptionRequired
VERCEL_API_TOKENVercel access tokenYes

🤝 Contributing

  1. Fork the repository
  2. Create feature branch (git checkout -b feature/amazing-feature)
  3. Commit changes (git commit -m 'Add amazing feature')
  4. Push to branch (git push origin feature/amazing-feature)
  5. Open Pull Request

📄 License

MIT License - see LICENSE for details

Comments

Markdown supported

New to MCP?

From what the protocol is to installing your first server, these answers cover most first questions.

An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.