How do I get a Pulumi Access Token?

Log in to the [**Pulumi Console**](https://app.pulumi.com), go to **Account Settings > Access Tokens**, click **Create Token**, give it a name and copy the token immediately — it starts with `pul_` and won't be shown again.

What is a Pulumi stack?

A stack is an isolated, independently configurable instance of your Pulumi program. Stacks typically represent different environments like dev, staging and prod. Each stack has its own configuration, state, outputs and deployment history. Use list_stacks to discover all stacks in an organization.

Can I view the outputs of a stack?

Yes! Use `get_stack_outputs` with the org name, project name and stack name. It returns all exported output values from the latest successful deployment, such as URLs, IP addresses, resource IDs and connection strings. This is useful for discovering endpoint addresses after infrastructure deployment.

Pulumi MCP Server for CrewAI 11 tools — connect in under 2 minutes

Name: Pulumi
Availability: InStock
Author: Vinkius

docs.crewai.com/mcp/overview

Built by Vinkius GDPR 11 Tools Framework

Connect your CrewAI agents to Pulumi through the Vinkius — pass the Edge URL in the `mcps` parameter and every Pulumi tool is auto-discovered at runtime. No credentials to manage, no infrastructure to maintain.

Get MCP Server for AI Agents

ASK AI ABOUT THIS MCP SERVER

Open in ChatGPT Open in Claude Open in Perplexity

Vinkius supports streamable HTTP and SSE.

python

from crewai import Agent, Task, Crew

agent = Agent(
    role="Pulumi Specialist",
    goal="Help users interact with Pulumi effectively",
    backstory=(
        "You are an expert at leveraging Pulumi tools "
        "for automation and data analysis."
    ),
    # Your Vinkius token — get it at cloud.vinkius.com
    mcps=["https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"],
)

task = Task(
    description=(
        "Explore all available tools in Pulumi "
        "and summarize their capabilities."
    ),
    agent=agent,
    expected_output=(
        "A detailed summary of 11 available tools "
        "and what they can do."
    ),
)

crew = Crew(agents=[agent], tasks=[task])
result = crew.kickoff()
print(result)

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Stream every event to Splunk, Datadog, or your own webhook in real-time

* 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.

When paired with CrewAI, Pulumi becomes a first-class tool in your multi-agent workflows. Each agent in the crew can call Pulumi tools autonomously — one agent queries data, another analyzes results, a third compiles reports — all orchestrated through the Vinkius with zero configuration overhead.

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 CrewAI 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 CrewAI via MCP

Follow these steps to integrate the Pulumi MCP Server with CrewAI.

Install CrewAI

Run pip install crewai

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token from cloud.vinkius.com

Customize the agent

Adjust the role, goal, and backstory to fit your use case

Run the crew

Run python crew.py — CrewAI auto-discovers 11 tools from Pulumi

Why Use CrewAI with the Pulumi MCP Server

CrewAI Multi-Agent Orchestration Framework provides unique advantages when paired with Pulumi through the Model Context Protocol.

Multi-agent collaboration lets you decompose complex workflows into specialized roles — one agent researches, another analyzes, a third generates reports — each with access to MCP tools

CrewAI's native MCP integration requires zero adapter code: pass the Vinkius Edge URL directly in the `mcps` parameter and agents auto-discover every available tool at runtime

Built-in task delegation and shared memory mean agents can pass context between steps without manual state management, enabling multi-hop reasoning across tool calls

Sequential and hierarchical crew patterns map naturally to real-world workflows: enumerate subdomains → analyze DNS history → check WHOIS records → compile findings into actionable reports

Pulumi + CrewAI Use Cases

Practical scenarios where CrewAI combined with the Pulumi MCP Server delivers measurable value.

Automated multi-step research: a reconnaissance agent queries Pulumi for raw data, then a second analyst agent cross-references findings and flags anomalies — all without human handoff

Scheduled intelligence reports: set up a crew that periodically queries Pulumi, analyzes trends over time, and generates executive briefings in markdown or PDF format

Multi-source enrichment pipelines: chain Pulumi tools with other MCP servers in the same crew, letting agents correlate data across multiple providers in a single workflow

Compliance and audit automation: a compliance agent queries Pulumi against predefined policy rules, generates deviation reports, and routes findings to the appropriate team

Pulumi MCP Tools for CrewAI (11)

These 11 tools become available when you connect Pulumi to CrewAI 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 CrewAI

Ready-to-use prompts you can give your CrewAI 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 CrewAI

Common issues when connecting Pulumi to CrewAI through the Vinkius, and how to resolve them.

MCP tools not discovered

Ensure the Edge URL is correct. CrewAI connects lazily when the crew starts — check console output.

Agent not using tools

Make the task description specific. Instead of "do something", say "Use the available tools to list contacts".

Timeout errors

CrewAI has a 10s connection timeout by default. Ensure your network can reach the Edge URL.

Rate limiting or 429 errors

The Vinkius enforces per-token rate limits. Check your subscription tier and request quota in the dashboard. Upgrade if you need higher throughput.

Pulumi + CrewAI FAQ

Common questions about integrating Pulumi MCP Server with CrewAI.

How does CrewAI discover and connect to MCP tools?

CrewAI connects to MCP servers lazily — when the crew starts, each agent resolves its MCP URLs and fetches the tool catalog via the standard tools/list method. This means tools are always fresh and reflect the server's current capabilities. No tool schemas need to be hardcoded.

Can different agents in the same crew use different MCP servers?

Yes. Each agent has its own mcps list, so you can assign specific servers to specific roles. For example, a reconnaissance agent might use a domain intelligence server while an analysis agent uses a vulnerability database server.

What happens when an MCP tool call fails during a crew run?

CrewAI wraps tool failures as context for the agent. The LLM receives the error message and can decide to retry with different parameters, fall back to a different tool, or mark the task as partially complete. This resilience is critical for production workflows.

Can CrewAI agents call multiple MCP tools in parallel?

CrewAI agents execute tool calls sequentially within a single reasoning step. However, you can run multiple agents in parallel using process=Process.parallel, each calling different MCP tools concurrently. This is ideal for workflows where separate data sources need to be queried simultaneously.

Can I run CrewAI crews on a schedule (cron)?

Yes. CrewAI crews are standard Python scripts, so you can invoke them via cron, Airflow, Celery, or any task scheduler. The crew.kickoff() method runs synchronously by default, making it straightforward to integrate into existing pipelines.