Bring Infrastructure As Code
to CrewAI
Create your Vinkius account to connect Pulumi to CrewAI and start using all 11 AI tools in minutes. Fully managed, enterprise secure, and ready to use without writing a single line of code. No hosting, no server setup — just connect and start using.
GDPR Free for SubscribersCompatible with every major AI agent and IDE
Gemini
What is the Pulumi MCP Server?
Connect your Pulumi account to any AI agent and take full control of your infrastructure-as-code through natural conversation.
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)
How it works
- Subscribe to this server
- Enter your Pulumi Access Token
- Start managing your infrastructure from Claude, Cursor, or any MCP-compatible client
No more switching to the Pulumi Console to check deployment status or review stack outputs. Your AI acts as a dedicated infrastructure operations engineer.
Who is this for?
- DevOps Engineers — quickly check stack status, review deployment history and inspect outputs without opening the Pulumi Console
- Platform Teams — audit infrastructure changes, track deployment success rates and manage stack tags across organizations
- Developers — discover available stacks, review exported endpoints and verify resource provisioning via conversation
Built-in capabilities (11)
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
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
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
Provide the org name, project name, stack name and deployment version number. Get details for a specific Pulumi deployment
Provide the organization name (slug). Get details for a specific Pulumi organization
Provide the org name, project name and stack name. Get details for a specific Pulumi stack
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
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
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
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
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
Why CrewAI?
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 Vinkius with zero configuration overhead.
- —
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
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CrewAI's native MCP integration requires zero adapter code: pass Vinkius Edge URL directly in the
mcpsparameter 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
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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 in CrewAI
Why run Pulumi with Vinkius?
The Pulumi connection runs on our fully managed, secure cloud infrastructure. We handle the hosting, maintenance, and security so you don't have to deal with servers or code. All 11 tools are ready to work instantly without any complex setup.
You stay in complete control of your data. Your AI only accesses the information you approve, keeping your sensitive passwords and private details completely safe. Plus, with automatic optimizations, your AI works faster and more efficiently.
* Every connection is hosted and maintained by Vinkius. We handle the security, updates, and infrastructure so you don't have to write code or manage servers. See our infrastructure
Over 4,000 integrations ready for AI agents
Explore a vast library of pre-built integrations, optimized and ready to deploy.
Connect securely in under 30 seconds
Generate tokens to authenticate and link external services in a single step.
Complete visibility into every agent action
Audit live requests, latency, success rates, and active security compliance policies.
Optimize spending and track token ROI
Analyze real-time token consumption and cost metrics detailed by connection.
Explore our live AI Agents Analytics dashboard to see it all working
This dashboard is included when you connect Pulumi using Vinkius. You will never be left in the dark about what your AI agents are doing with your tools.
Pulumi and 4,000+ other AI tools. No hosting, no code, ready to use.
Professionals who connect Pulumi to CrewAI through Vinkius don't need to write code, manage servers, or worry about security. Everything is pre-configured, secure, and runs automatically in the background.
Raw MCP | Vinkius | |
|---|---|---|
| Ready-to-use MCPs | Find and configure each manually | 4,000+ MCPs ready to use |
| Connection Setup | Manual coding & server setup | 1-click instant connection |
| Server Hosting | You host it yourself (needs 24/7 uptime) | 100% hosted & managed by Vinkius |
| Security & Privacy | Stored in plaintext config files | Bank-grade encrypted vault |
| Activity Visibility | Blind execution (no logs or tracking) | Live dashboard with real-time logs |
| Cost Control | Runaway AI token spend risk | Automatic budget limits |
| Revoking Access | Must delete files or code to stop | 1-click disconnect button |
How Vinkius secures
Pulumi for CrewAI
Every request between CrewAI and Pulumi is protected by our secure gateway. We automatically keep your sensitive data private, prevent unauthorized access, and let you disconnect instantly at any time.
Frequently asked questions
How do I get a Pulumi Access Token?
Log in to the Pulumi Console, 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 see the deployment history of a stack?
Yes! Use list_deployments with the org name, project name and stack name. It returns the update history showing version number, status (succeeded, failed, in-progress), start/end time and resource change counts. Use get_deployment with a specific version for detailed logs and error messages.
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.
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.
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
Vinkius enforces per-token rate limits. Check your subscription tier and request quota in the dashboard. Upgrade if you need higher throughput.
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