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Pulumi MCP Server for Vercel AI SDK 11 tools — connect in under 2 minutes

Built by Vinkius GDPR 11 Tools SDK

The Vercel AI SDK is the TypeScript toolkit for building AI-powered applications. Connect Pulumi through Vinkius and every tool is available as a typed function. ready for React Server Components, API routes, or any Node.js backend.

Vinkius supports streamable HTTP and SSE.

typescript
import { createMCPClient } from "@ai-sdk/mcp";
import { generateText } from "ai";
import { openai } from "@ai-sdk/openai";

async function main() {
  const mcpClient = await createMCPClient({
    transport: {
      type: "http",
      // Your Vinkius token. get it at cloud.vinkius.com
      url: "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp",
    },
  });

  try {
    const tools = await mcpClient.tools();
    const { text } = await generateText({
      model: openai("gpt-4o"),
      tools,
      prompt: "Using Pulumi, list all available capabilities.",
    });
    console.log(text);
  } finally {
    await mcpClient.close();
  }
}

main();
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About Pulumi MCP Server

Connect your Pulumi account to any AI agent and take full control of your infrastructure-as-code through natural conversation.

The Vercel AI SDK gives every Pulumi tool full TypeScript type inference, IDE autocomplete, and compile-time error checking. Connect 11 tools through Vinkius and stream results progressively to React, Svelte, or Vue components. works on Edge Functions, Cloudflare Workers, and any Node.js runtime.

What you can do

  • Organization Discovery — List organizations and retrieve their details, team settings and member info
  • Stack Management — List, create and delete stacks (infrastructure environments) across all your projects
  • Deployment Tracking — Monitor stack update history with status (succeeded, failed, in-progress), resource changes and error logs
  • Output Inspection — View exported output values from the latest deployment (URLs, IPs, resource IDs)
  • Tag Management — List and set custom tags on stacks for organization and filtering (environment, team, cost-center)

The Pulumi MCP Server exposes 11 tools through the Vinkius. Connect it to Vercel AI SDK in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.

How to Connect Pulumi to Vercel AI SDK via MCP

Follow these steps to integrate the Pulumi MCP Server with Vercel AI SDK.

01

Install dependencies

Run npm install @ai-sdk/mcp ai @ai-sdk/openai

02

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token

03

Run the script

Save to agent.ts and run with npx tsx agent.ts

04

Explore tools

The SDK discovers 11 tools from Pulumi and passes them to the LLM

Why Use Vercel AI SDK with the Pulumi MCP Server

Vercel AI SDK provides unique advantages when paired with Pulumi through the Model Context Protocol.

01

TypeScript-first: every MCP tool gets full type inference, IDE autocomplete, and compile-time error checking out of the box

02

Framework-agnostic core works with Next.js, Nuxt, SvelteKit, or any Node.js runtime. same Pulumi integration everywhere

03

Built-in streaming UI primitives let you display Pulumi tool results progressively in React, Svelte, or Vue components

04

Edge-compatible: the AI SDK runs on Vercel Edge Functions, Cloudflare Workers, and other edge runtimes for minimal latency

Pulumi + Vercel AI SDK Use Cases

Practical scenarios where Vercel AI SDK combined with the Pulumi MCP Server delivers measurable value.

01

AI-powered web apps: build dashboards that query Pulumi in real-time and stream results to the UI with zero loading states

02

API backends: create serverless endpoints that orchestrate Pulumi tools and return structured JSON responses to any frontend

03

Chatbots with tool use: embed Pulumi capabilities into conversational interfaces with streaming responses and tool call visibility

04

Internal tools: build admin panels where team members interact with Pulumi through natural language queries

Pulumi MCP Tools for Vercel AI SDK (11)

These 11 tools become available when you connect Pulumi to Vercel AI SDK via MCP:

01

create_stack

A stack is an isolated, independently configurable instance of your Pulumi program. Requires the org name, project name and stack name (e.g. "staging", "prod"). Returns the created stack with its URL. Create a new Pulumi stack

02

delete_stack

The stack must be empty (no resources) or force deletion must be enabled. Provide the org name, project name and stack name. WARNING: this action is irreversible. Delete a Pulumi stack

03

get_current_user

Returns the user's GitHub login, avatar URL, email and name. Use this to verify your access token is working correctly and to see which identity the API calls will appear as. Get the currently authenticated Pulumi user

04

get_deployment

Provide the org name, project name, stack name and deployment version number. Get details for a specific Pulumi deployment

05

get_organization

Provide the organization name (slug). Get details for a specific Pulumi organization

06

get_stack

Provide the org name, project name and stack name. Get details for a specific Pulumi stack

07

get_stack_outputs

Outputs are values your Pulumi program exports, such as URLs, IP addresses, resource IDs and connection strings. Useful for discovering endpoint addresses and configuration values after infrastructure deployment. Get the exported output values from a Pulumi stack

08

list_deployments

Each deployment shows its version number, status (succeeded, failed, in-progress), start/end time, resource changes (created, updated, deleted) and the user who triggered it. Use this to audit infrastructure changes and track deployment success/failure patterns. List deployment history for a Pulumi stack

09

list_stack_tags

Tags are key-value metadata labels used for organizing, filtering and managing stacks (e.g. environment=prod, team=platform, cost-center=engineering). List tags on a Pulumi stack

10

list_stacks

Each stack represents an isolated, independently configurable instance of your infrastructure (e.g. dev, staging, prod). Returns stack name, project name, last update info, resource count and whether updates are in progress. List all stacks in a Pulumi organization

11

set_stack_tag

Tags are used for organizing, filtering and managing stacks (e.g. key="environment", value="prod", key="team", value="platform"). Provide the org name, project name, stack name, tag name and tag value. Set a tag on a Pulumi stack

Example Prompts for Pulumi in Vercel AI SDK

Ready-to-use prompts you can give your Vercel AI SDK agent to start working with Pulumi immediately.

01

"Show me all stacks in my organization."

02

"What was the result of the latest deployment to my-infra/prod?"

03

"Show me the exported outputs from the prod stack."

Troubleshooting Pulumi MCP Server with Vercel AI SDK

Common issues when connecting Pulumi to Vercel AI SDK through the Vinkius, and how to resolve them.

01

createMCPClient is not a function

Install: npm install @ai-sdk/mcp

Pulumi + Vercel AI SDK FAQ

Common questions about integrating Pulumi MCP Server with Vercel AI SDK.

01

How does the Vercel AI SDK connect to MCP servers?

Import createMCPClient from @ai-sdk/mcp and pass the server URL. The SDK discovers all tools and provides typed TypeScript interfaces for each one.
02

Can I use MCP tools in Edge Functions?

Yes. The AI SDK is fully edge-compatible. MCP connections work on Vercel Edge Functions, Cloudflare Workers, and similar runtimes.
03

Does it support streaming tool results?

Yes. The SDK provides streaming primitives like useChat and streamText that handle tool calls and display results progressively in the UI.

Connect Pulumi to Vercel AI SDK

Get your token, paste the configuration, and start using 11 tools in under 2 minutes. No API key management needed.