Pulumi MCP Server for Pydantic AI 11 tools — connect in under 2 minutes
Pydantic AI brings type-safe agent development to Python with first-class MCP support. Connect Pulumi through the Vinkius and every tool is automatically validated against Pydantic schemas — catch errors at build time, not in production.
ASK AI ABOUT THIS MCP SERVER
Vinkius supports streamable HTTP and SSE.
import asyncio
from pydantic_ai import Agent
from pydantic_ai.mcp import MCPServerHTTP
async def main():
# Your Vinkius token — get it at cloud.vinkius.com
server = MCPServerHTTP(url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")
agent = Agent(
model="openai:gpt-4o",
mcp_servers=[server],
system_prompt=(
"You are an assistant with access to Pulumi "
"(11 tools)."
),
)
result = await agent.run(
"What tools are available in Pulumi?"
)
print(result.data)
asyncio.run(main())
* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure
About Pulumi MCP Server
Connect your Pulumi account to any AI agent and take full control of your infrastructure-as-code through natural conversation.
Pydantic AI validates every Pulumi tool response against typed schemas, catching data inconsistencies at build time. Connect 11 tools through the Vinkius and switch between OpenAI, Anthropic, or Gemini without changing your integration code — full type safety, structured output guarantees, and dependency injection for testable agents.
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 Pydantic AI 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 Pydantic AI via MCP
Follow these steps to integrate the Pulumi MCP Server with Pydantic AI.
Install Pydantic AI
Run pip install pydantic-ai
Replace the token
Replace [YOUR_TOKEN_HERE] with your Vinkius token
Run the agent
Save to agent.py and run: python agent.py
Explore tools
The agent discovers 11 tools from Pulumi with type-safe schemas
Why Use Pydantic AI with the Pulumi MCP Server
Pydantic AI provides unique advantages when paired with Pulumi through the Model Context Protocol.
Full type safety: every MCP tool response is validated against Pydantic models, catching data inconsistencies before they reach your application
Model-agnostic architecture — switch between OpenAI, Anthropic, or Gemini without changing your Pulumi integration code
Structured output guarantee: Pydantic AI ensures tool results conform to defined schemas, eliminating runtime type errors
Dependency injection system cleanly separates your Pulumi connection logic from agent behavior for testable, maintainable code
Pulumi + Pydantic AI Use Cases
Practical scenarios where Pydantic AI combined with the Pulumi MCP Server delivers measurable value.
Type-safe data pipelines: query Pulumi with guaranteed response schemas, feeding validated data into downstream processing
API orchestration: chain multiple Pulumi tool calls with Pydantic validation at each step to ensure data integrity end-to-end
Production monitoring: build validated alert agents that query Pulumi and output structured, schema-compliant notifications
Testing and QA: use Pydantic AI's dependency injection to mock Pulumi responses and write comprehensive agent tests
Pulumi MCP Tools for Pydantic AI (11)
These 11 tools become available when you connect Pulumi to Pydantic AI via MCP:
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
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
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
get_deployment
Provide the org name, project name, stack name and deployment version number. Get details for a specific Pulumi deployment
get_organization
Provide the organization name (slug). Get details for a specific Pulumi organization
get_stack
Provide the org name, project name and stack name. Get details for a specific Pulumi stack
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
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
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
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
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 Pydantic AI
Ready-to-use prompts you can give your Pydantic AI agent to start working with Pulumi immediately.
"Show me all stacks in my organization."
"What was the result of the latest deployment to my-infra/prod?"
"Show me the exported outputs from the prod stack."
Troubleshooting Pulumi MCP Server with Pydantic AI
Common issues when connecting Pulumi to Pydantic AI through the Vinkius, and how to resolve them.
MCPServerHTTP not found
pip install --upgrade pydantic-aiPulumi + Pydantic AI FAQ
Common questions about integrating Pulumi MCP Server with Pydantic AI.
How does Pydantic AI discover MCP tools?
MCPServerHTTP instance with the server URL. Pydantic AI connects, discovers all tools, and generates typed Python interfaces automatically.Does Pydantic AI validate MCP tool responses?
Can I switch LLM providers without changing MCP code?
Connect Pulumi with your favorite client
Step-by-step setup guides for every MCP-compatible client and framework:
Anthropic's native desktop app for Claude with built-in MCP support.
AI-first code editor with integrated LLM-powered coding assistance.
GitHub Copilot in VS Code with Agent mode and MCP support.
Purpose-built IDE for agentic AI coding workflows.
Autonomous AI coding agent that runs inside VS Code.
Anthropic's agentic CLI for terminal-first development.
Python SDK for building production-grade OpenAI agent workflows.
Google's framework for building production AI agents.
Type-safe agent development for Python with first-class MCP support.
TypeScript toolkit for building AI-powered web applications.
TypeScript-native agent framework for modern web stacks.
Python framework for orchestrating collaborative AI agent crews.
Leading Python framework for composable LLM applications.
Data-aware AI agent framework for structured and unstructured sources.
Microsoft's framework for multi-agent collaborative conversations.
Connect Pulumi to Pydantic AI
Get your token, paste the configuration, and start using 11 tools in under 2 minutes. No API key management needed.