Cloudify MCP. Audit multi-cloud infrastructure state on demand.
Gemini Works with every AI agent you already use
…and any MCP-compatible client
Just plug in your AI agents and start using Vinkius.
Cloudify lets you manage and audit complex multi-cloud infrastructure from any AI client. Instead of diving into multiple console dashboards, you talk to your agent and get instant status checks on blueprints, live deployments, and running workflows across AWS, GCP, and more.
What your AI agents can do
Get blueprint
Extracts the structural properties that define an active blueprint schema.
Get deployment
Pulls explicit internal data showing the precise execution topology of a deployment.
List blueprints
Lists all available blueprint templates that manage top-level orchestration schemas.
List all available blueprint templates and get deep structural details about any specific one.
Retrieve the current, precise status of deployed infrastructure schemas across environments.
View real-time records of installation, uninstallation, or healing transactions to pinpoint when things went wrong.
Identify specific infrastructure components and check their current lifecycle status (started, deleted, etc.).
Discover all installed cloud integrations or Python abstractions used by the manager.
Ask AI about this MCP
Supported MCP Clients
OAuth 2.0 Compatible Gemini Waiting for input…
Cloudify: 7 Tools for Infra Management
These tools let your agent read the full state of your cloud infrastructure, from high-level blueprints down to individual running nodes.
Make your AI actually useful.
Add this MCP to Claude, Cursor, or Windsurf and your AI stops guessing. It gets real tools to look things up, take action, and handle the stuff you keep doing by hand.
Start using Cloudify on Vinkius019d7574get blueprint
Extracts the structural properties that define an active blueprint schema.
019d7574get deployment
Pulls explicit internal data showing the precise execution topology of a deployment.
019d7574list blueprints
Lists all available blueprint templates that manage top-level orchestration schemas.
019d7574list deployments
Retrieves the current structural state, confirming active runtime schemas.
019d7574list executions
Identifies all recent and active cluster limits related to deployment workflows.
019d7574list nodes
Finds specific infrastructure components and checks their current operational rules and status.
019d7574list plugins
Extracts a list of all installed capabilities, mapping native cloud integrations.
Choose How to Get Started
Build a custom MCP for your own tools, or connect a ready-made integration from our catalog.
Build Your Own
Turn any API into an MCP. Import a spec, define Agent Skills, or deploy with MCPFusion.
- Import from OpenAPI, Swagger, or YAML specs
- Create Agent Skills with progressive disclosure
- Deploy to edge with MCPFusion framework
- Built in DLP, auth, and compliance on every call
- Real time usage dashboard and cost metering
- Publish to catalog or keep private
Make Your AI Do More
Start with Cloudify, then connect any of our 4,800+ other servers whenever your AI needs more. One click, no limits.
- Use this MCP plus 4,800+ others, all in one place
- Add new capabilities to your AI anytime you want
- Every connection is secured and compliant automatically
- Track usage and costs across all your servers
- Works with Claude, ChatGPT, Cursor, and more
- New servers added to the catalog every week
Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Cloudify. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.
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Works with Claude, ChatGPT, Cursor, and more
The Model Context Protocol standardizes how applications expose capabilities to LLMs. Instead of operating in isolation, your AI gains direct access to external platforms, live data, and real-world actions through secure, standardized connections.
This server provides 7 capabilities that interface natively with Claude, ChatGPT, Cursor, and any MCP client. No middleware. No custom integration required.
The pain of dashboard hopping
Right now, finding out why a deployment broke means logging into five different web consoles. You check the CI/CD pipeline for execution status; then you jump to the networking console to see if nodes are communicating; finally, you open the resource manager just to verify the blueprint version. It's clicking through tabs and copying names back and forth until your eyes hurt.
With this MCP, your agent does that work in plain English. You simply tell it what's wrong—for example, 'What failed with the web app deployment?'—and you get a consolidated answer, linking the blueprint definition to the live node status without leaving your chat.
Getting clear audit data with `list_blueprints`
Manually auditing blueprints means downloading template files and trying to map complex YAML structures by hand. You spend hours comparing the 'as-designed' state against what actually exists in a live environment, often missing subtle structural differences.
Now, you can simply ask your agent to list all available blueprints. It extracts those deep properties for you instantly. You get a clear record of what templates are even possible, which saves massive amounts of time.
What you can do with this MCP connector
Need visibility into a sprawling cloud setup? This MCP connects your AI agent directly to the Cloudify Manager, giving you read-only control over complex, multi-cloud orchestration. You can audit entire infrastructure stacks through natural conversation. Need to check if a specific cluster failed during deployment? Ask about it. Want to know what templates are even available for use? The system handles that retrieval automatically.
It's not just listing things; you track the actual state of your environment. You can monitor every step—from an install transaction to a healing process—without ever leaving your chat window. Because this connection runs through Vinkius, you get full visibility into everything your agent does. The platform keeps a detailed audit trail showing exactly which blueprints and nodes were queried and what data flowed out.
It means nothing happens in the dark; every step is logged and traceable.
019d7574-3760-7126-b17a-71ff4649a38c 
How Cloudify MCP Works
- 1 Subscribe to this MCP and provide your Cloudify Manager URL and API Token.
- 2 Connect your AI agent from your preferred client (like Cursor or Claude).
- 3 Ask the agent a question, like 'What's the status of the production database?' and get an immediate answer.
The bottom line is that you use natural language to query highly specific infrastructure data without ever touching the Cloudify UI.
Who Is Cloudify MCP For?
Anyone who has to look at a dozen different dashboards just to figure out why one service broke. The ops engineer, the SRE, and the platform architect who spends more time debugging than coding.
Troubleshoot failed deployments by cross-referencing node states with execution logs to find the exact point of failure.
Audit multi-cloud integration points, validating that all necessary plugins are installed and configured correctly across different environments.
Check the full lifecycle of a new service blueprint, ensuring all dependencies (like specific schemas or resources) are accounted for before deployment.
What Changes When You Connect
- Pinpoint the problem immediately. Instead of manually checking a dashboard, you can ask your agent to list nodes and check their status instantly, finding which specific instance is failing.
- Understand design intent vs. reality. Use
list_blueprintsfirst to see the intended schema, then uselist_deploymentsto confirm what was actually launched in the cloud. - Zero guesswork on failures. By checking active workflow limits using
list_executions, you get a full history of installations and healing transactions without digging through logs. - Know your integrations. Need to check if AWS or GCP support is configured? Running
list_pluginsgives you an inventory of all installed cloud abstractions in one go. - Faster debugging loops. Combining status checks like calling
get_deploymentwith node inspections lets you diagnose complex issues by linking the high-level deployment target directly to a failing component.
Real-World Use Cases
The database replica suddenly goes down.
You ask your agent, 'What nodes are currently in an error state for the primary DB cluster?' The agent uses list_nodes and immediately tells you that two replicas failed their health check. You don't have to open the cloud console.
We need to launch a completely new microservice.
You ask, 'What blueprint do we use for a standard containerized service?' The agent runs list_blueprints and provides the exact name, letting you know exactly where to start your deployment.
The CI/CD pipeline failed mysteriously.
You ask, 'Show me all recent workflow executions for the web app.' The agent uses list_executions, showing you that a manual heal transaction was attempted but failed due to resource limits.
Auditing compliance across environments.
You ask, 'What cloud integrations are active?' The agent runs list_plugins, giving you an immediate inventory of all installed capabilities like AWS and GCP abstraction layers for auditing purposes.
The Tradeoffs
Treating it like a simple lookup
Just asking 'What are my blueprints?' without context. You get a list, but no idea which ones are active or what they affect.
→
Always follow up by checking the live status using list_deployments to confirm if any of those listed blueprints have actually been instantiated in the cloud.
Assuming write capability
Asking the agent, 'Fix the broken node.' The tool only provides read-only data and cannot make changes.
→
Use this MCP to diagnose by calling list_nodes or get_deployment. Once you know what is wrong, then manually fix it via the native UI.
Missing the full picture
Only checking node status (list_nodes) when the failure might be related to how the service was originally configured.
→
Start by calling get_blueprint first. Understanding the blueprint's structural properties is key context for why a specific node failed.
When It Fits, When It Doesn't
Use this if you need absolute certainty about your infrastructure's current state, history, or definition. You should use it when troubleshooting: 'Why did X fail?' or 'What was the intended configuration for Y?'. This MCP is strictly diagnostic; all its tools are read-only and provide audit data only. Don't use it if you need to change a resource (e.g., change firewall rules, scale up replicas). For changes, stick to your native cloud consoles. If you just need to list available resources, list_blueprints or list_plugins are the right tools.
Common Questions About Cloudify MCP
How do I use the list_blueprints tool in Cloudify? +
You ask your agent to run list_blueprints. It reads all available top-level orchestration schemas, letting you see every blueprint template managed by the system.
Can I check if a node is running with list_nodes? +
Yes. You query for specific nodes using list_nodes. This function identifies exact components and shows their current lifecycle properties, like whether they are started or deleted.
What does get_deployment do? +
The get_deployment tool extracts the explicit internal state of a deployed service. It pulls the precise execution topology so you know exactly which resources were created during deployment.
How do I track old failures with list_executions? +
Use list_executions. This function identifies all active cluster limits across deployment workflows, giving you a history of installation or healing attempts and their outcomes.
What credentials are needed before I can use the `list_plugins` tool? +
You need your Cloudify Manager URL and an API Token. Vinkius handles this connection using a zero-trust proxy, meaning your keys pass through in transit but are never stored on disk.
How does the `get_blueprint` tool extract properties for me? +
It performs structural extraction of properties that drive active blueprint schemas. This gives you the underlying definitions and attributes, not just a simple list name.
If I run `list_deployments`, are there limits on how many runtime schemas I can check? +
Vinkius manages rate limiting for this MCP. If your agent attempts too many calls at once, the system pauses and notifies your client immediately, preventing service disruption.
When I run `list_blueprints`, what scope information can I get? +
It identifies bounded logical arrays that manage top-level orchestration schemas. The results provide scope details so you know exactly which environments the blueprint is intended for.
Use it with your favorite AI tools
Connect this server to Cursor, Claude, VS Code, and more.