Vercel Agent Skills: Complete Guide (2026)

Vercel Agent Skills: Complete Guide (2026)

Vercel Agent Skills let AI coding agents interact directly with Vercel's platform -- deploying previews, managing environment variables, checking build logs, and running diagnostics, all from within your AI-assisted development workflow. Instead of switching between your editor and the Vercel dashboard, your AI agent handles the deployment lifecycle for you.

This guide covers what Agent Skills are, how to set them up, and how to use them effectively with popular AI coding agents.

What Are Vercel Agent Skills?

Agent Skills are a set of tools that Vercel exposes via the Model Context Protocol (MCP). When an AI coding agent (like Claude Code, Cursor, or Cline) connects to Vercel's MCP server, it gains the ability to:

• Trigger and monitor deployments
• Read build and runtime logs
• Manage environment variables
• Inspect project configuration
• Run Web Vitals diagnostics
• Manage domains and DNS settings

Think of Agent Skills as giving your AI assistant admin access to your Vercel dashboard, controlled entirely through natural language.

Setting Up Vercel Agent Skills

Prerequisites

• A Vercel account with at least one project
• A Vercel API token (Settings > Tokens)
• An AI coding agent that supports MCP

Step 1: Generate a Vercel Token

Go to vercel.com/account/tokens and create a new token:

• Name: Agent Skills
• Scope: Your team or personal account
• Expiration: Choose based on your security requirements

Step 2: Configure MCP

The configuration depends on which AI agent you use.

For Claude Code (claude_desktop_config.json):

{
  "mcpServers": {
    "vercel": {
      "command": "npx",
      "args": ["-y", "vercel-mcp-server"],
      "env": {
        "VERCEL_TOKEN": "your_token_here"
      }
    }
  }
}

For Cursor (.cursor/mcp.json):

{
  "mcpServers": {
    "vercel": {
      "command": "npx",
      "args": ["-y", "vercel-mcp-server"],
      "env": {
        "VERCEL_TOKEN": "your_token_here"
      }
    }
  }
}

For Cline (VS Code settings):

Add the MCP server configuration through Cline's MCP settings panel with the same command and environment variables.

Step 3: Verify the Connection

After configuring the MCP server, test it by asking your AI agent:

"List my Vercel projects"

If configured correctly, the agent should return a list of your Vercel projects.

Available Agent Skills

Deployment Skills

Skill	Description	Example Prompt
list_deployments	List recent deployments	"Show my last 5 deployments"
get_deployment	Get deployment details	"What's the status of the latest deployment?"
create_deployment	Trigger a new deployment	"Deploy the current branch to preview"
get_deployment_logs	Read build and runtime logs	"Show me the build logs for the failed deployment"
cancel_deployment	Cancel an in-progress build	"Cancel the current deployment"

Project Management Skills

Skill	Description	Example Prompt
list_projects	List all projects	"List my Vercel projects"
get_project	Get project configuration	"Show me the config for project my-app"
update_project	Update project settings	"Set the Node.js version to 22 for this project"
list_domains	List project domains	"What domains are configured for this project?"

Environment Variable Skills

Skill	Description	Example Prompt
list_env_vars	List environment variables	"Show all env vars for production"
create_env_var	Add a new environment variable	"Add DATABASE_URL to production"
update_env_var	Update an existing variable	"Update the API_KEY for staging"
delete_env_var	Remove a variable	"Remove the deprecated LEGACY_KEY variable"

Diagnostics Skills

Skill	Description	Example Prompt
get_web_vitals	Check Core Web Vitals	"How are the Web Vitals for my-app?"
get_speed_insights	Get performance data	"Show speed insights for the last 7 days"
check_dns	Verify DNS configuration	"Check if DNS is configured correctly for my domain"

Practical Workflows

Workflow 1: Deploy and Verify

Here is a typical conversation with an AI agent using Vercel skills:

You: "Deploy the current branch and show me the build logs"

Agent: I'll trigger a deployment and monitor it.
[Calls create_deployment]
Deployment started: dpl_abc123
[Calls get_deployment_logs with polling]

Build completed in 42s. Here's the summary:
- Framework: Next.js 15.2
- Build time: 42s
- Output size: 2.3 MB
- Preview URL: https://my-app-abc123.vercel.app

No errors or warnings in the build logs.

Workflow 2: Debug a Failed Deployment

You: "The latest deployment failed. What happened?"

Agent: Let me check the deployment logs.
[Calls list_deployments]
[Calls get_deployment_logs for the latest failed deployment]

The deployment failed during the build step. Here's the error:

Error: Module not found: Can't resolve '@/components/NewFeature'

This file is imported in src/app/page.tsx:14 but doesn't exist.
You need to either create the component or remove the import.

Workflow 3: Environment Variable Management

You: "Add the Stripe keys for production.
      STRIPE_PUBLIC_KEY=pk_live_xxx
      STRIPE_SECRET_KEY=sk_live_xxx"

Agent: I'll add both environment variables to the production environment.
[Calls create_env_var for STRIPE_PUBLIC_KEY, target: production]
[Calls create_env_var for STRIPE_SECRET_KEY, target: production]

Done. Both variables are now set for the production environment:
- STRIPE_PUBLIC_KEY: pk_live_xxx (production)
- STRIPE_SECRET_KEY: sk_live_xxx (production, encrypted)

Note: You'll need to redeploy for these to take effect.
Shall I trigger a production deployment?

Workflow 4: Performance Monitoring

You: "Check the Web Vitals for our production site"

Agent: [Calls get_web_vitals for production]

Here are the Core Web Vitals for the last 28 days:

| Metric | Value | Rating |
|--------|-------|--------|
| LCP | 1.8s | Good |
| INP | 120ms | Good |
| CLS | 0.08 | Needs Improvement |

CLS is slightly above the 0.05 threshold. The main contributor
is a layout shift from your hero image loading without
explicit width/height attributes.

Creating Custom Skills

You can extend Vercel Agent Skills by wrapping the Vercel API in custom MCP tools. Here is an example using the MCP SDK:

import { McpServer } from "@modelcontextprotocol/sdk/server/mcp.js";
import { z } from "zod";

const server = new McpServer({
  name: "custom-vercel-skills",
  version: "1.0.0",
});

// Custom skill: Compare two deployments
server.tool(
  "compare_deployments",
  "Compare build times and sizes between two deployments",
  {
    deployment1: z.string().describe("First deployment ID"),
    deployment2: z.string().describe("Second deployment ID"),
  },
  async ({ deployment1, deployment2 }) => {
    const [d1, d2] = await Promise.all([
      fetchDeployment(deployment1),
      fetchDeployment(deployment2),
    ]);

    return {
      content: [{
        type: "text",
        text: JSON.stringify({
          buildTimeDiff: d2.buildTime - d1.buildTime,
          sizeDiff: d2.outputSize - d1.outputSize,
          deployment1: { buildTime: d1.buildTime, size: d1.outputSize },
          deployment2: { buildTime: d2.buildTime, size: d2.outputSize },
        }, null, 2),
      }],
    };
  }
);

Best Practices

1. Use Scoped Tokens

Create tokens with the minimum required permissions. If your agent only needs read access to deployments, do not give it write access to environment variables.

2. Set Up Team-Level Tokens

For teams, use a team-scoped token rather than personal tokens. This ensures access persists when team members leave.

3. Review Before Deploying to Production

While Agent Skills can deploy to production, set up a confirmation step in your workflow:

You: "Deploy to production"
Agent: This will deploy to production (my-app.com).
       Current preview is healthy with all checks passing.
       Do you want to proceed?
You: "Yes, go ahead"

4. Combine with Git Workflows

Agent Skills work best alongside proper Git workflows. Use them to:

• Deploy feature branches for preview
• Check build status after pushing
• Verify Web Vitals after production deployments
• Manage environment variables during onboarding

Troubleshooting

"Tool not found" error: Make sure the MCP server is running. Restart your AI agent after configuration changes.

"Unauthorized" error: Your Vercel token may be expired or lack permissions. Generate a new token with the correct scope.

"Project not found" error: Verify the project name or ID matches exactly. Use list_projects first to see available projects.

Conclusion

Vercel Agent Skills bridge the gap between AI coding agents and your deployment platform. By connecting your AI assistant to Vercel via MCP, you can manage deployments, environment variables, and performance monitoring without leaving your development flow.

For developers building AI-powered media applications on Vercel, Hypereal AI offers APIs for AI image and video generation that deploy seamlessly as Vercel serverless functions, creating a smooth pipeline from code to production.