Supercharge your AI with Cloudify. Manage multi-cloud deployment states via chat.
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…and any MCP-compatible client








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Cloudify MCP lets your AI agent manage multi-cloud infrastructure directly from natural conversation. You can audit complex blueprints, track live deployment states across AWS and GCP, monitor workflow executions in real time, and inspect deeply nested nodes without logging into the manager UI.
What your AI can do
List blueprints
Identifies logical arrays containing all top-level orchestration schemas available in the system.
Get blueprint
Extracts the structural properties that define active blueprint schemas for analysis.
List deployments
Retrieves a structural match verifying all actualized runtime schemas currently deployed.
Retrieve and list the structural properties that drive your multi-cloud orchestration templates.
Pull precise execution topologies to verify the current state of a live deployment.
View real-time records of installation, uninstallation, and healing transactions across environments.
Resolve deeply nested infrastructure elements and audit their specific lifecycle properties (created, deleted, etc.).
Discover which Python abstractions are currently mapped for AWS, GCP, or other cloud services.
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Compatible AI Apps
OAuth 2.0 CompatibleWaiting for input…
Cloudify MCP: 7 Tools for Infrastructure Management
Use these seven tools to audit schemas, check deployment states, list resource types, and manage cloud integrations programmatically.
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 VinkiusList Blueprints
Identifies logical arrays containing all top-level orchestration schemas available in the system.
Get Blueprint
Extracts the structural properties that define active blueprint schemas for analysis.
List Deployments
Retrieves a structural match verifying all actualized runtime schemas currently...
Get Deployment
Pulls explicit internal structural states to get a precise topology of an execution.
List Executions
Identifies precise, active cluster limits that define the boundaries of deployment...
List Nodes
Finds exact literal limits representing specific instances within the orchestration ruleset.
List Plugins
Extracts a list of explicit capabilities mapping for native cloud integrations like AWS and GCP.
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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
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- Built in DLP, auth, and compliance on every call
- Real time usage dashboard and cost metering
- Publish to catalog or keep private
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Start with Cloudify, then connect any of our 5,000+ other servers whenever your AI needs more. One click, no limits.
- Use this MCP plus 5,000+ others, all in one place
- Add new capabilities to your AI anytime you want
- Every connection is secured and compliant automatically
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- Works with Claude, ChatGPT, Cursor, and more
- New servers added to the catalog every week
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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 connection provides 7 powerful capabilities that interface natively with Claude, ChatGPT, Cursor, and other compatible AI platforms. No middleware. No custom integration required.
Checking cloud status means logging into four separate dashboards.
Today, figuring out if a complex deployment worked involves bouncing between the Cloudify Manager UI, checking deployment logs in one tab, and then cross-referencing node status in another. You copy IDs here, paste them there, check the timestamps, and pray you didn't miss an error state somewhere.
With this MCP, your agent handles the whole audit trail conversationally. It pulls together structural match data from `list_deployments` and combines it with real-time workflow events from `list_executions`. You get a single answer: what happened, when it happened, and if everything is healthy.
Audit complex infrastructure blueprints using the Cloudify MCP.
Manually auditing templates means finding the right blueprint ID, then clicking into the template view to see its root structure, and finally exporting that data just to share it with a teammate. It's tedious, time-consuming work.
The `list_blueprints` tool instantly gives you all available schemas, while `get_blueprint` extracts the full structural properties in plain text for your agent. You don't copy anything; you just ask.
What your AI can actually do with this
This connector gives you full control over complex cloud deployments via Cloudify Manager. Instead of navigating deep menus or running CLI commands for every state check, your agent handles it all conversationally. You can list available blueprints to find a specific template, track whether a deployment is finished by pulling its runtime schema, and monitor the live status of any installation or healing workflow.
It also lets you inspect individual infrastructure nodes, checking properties like 'started' or 'deleted', and audit which cloud plugins are installed for AWS or GCP. Connecting this MCP through Vinkius makes it available to your AI agent alongside thousands of other services, keeping all your operational tools in one place.
019d7574-3760-7126-b17a-71ff4649a38c Here's how it actually works
The bottom line is you get operational visibility into your entire multi-cloud stack without ever touching the Cloudify Manager UI.
Subscribe to this MCP and provide your Cloudify Manager URL and API Token credentials.
Your AI agent uses the connection data to query specific infrastructure components (blueprints, deployments) through natural language prompts.
The agent returns structured JSON containing the requested state—whether it's a list of nodes or an execution history.
Who is this actually for?
This MCP is for platform architects and DevOps engineers who are tired of manually checking dashboards or running multiple scripts just to figure out if a deployment failed. If you manage anything multi-cloud, this saves you hours.
Uses the MCP to track workflow executions and node states after an automated rollout, ensuring everything stayed healthy.
Audits multi-cloud integrations by listing installed plugins and checking blueprint structures across different environments.
Quickly identifies failed execution records or verifies infrastructure lifecycle states without digging through logs at 2 a.m.
What Changes When You Connect
Instantly audit your templates. Instead of manually listing blueprints, use list_blueprints to get a quick overview of every top-level schema you're running.
Track live changes without clicking through tabs. You can monitor installation or healing transactions by calling list_executions, getting real-time event logs immediately.
Deep dive into infrastructure elements. Need to know if a specific resource started, failed, or was deleted? Use list_nodes to audit its precise lifecycle properties.
Verify deployed state quickly. Don't trust the dashboard; run list_deployments to pull the exact structural match and confirm your runtime schemas are correct.
Understand cloud connectivity. You can use list_plugins to see which AWS or GCP abstractions are configured, making auditing multi-cloud setups simple.
See it in action
Post-incident root cause analysis
The SRE team needs to know why the 'web-app' deployment failed yesterday. They ask their agent to run list_executions for that service. The agent finds a recent 'heal' transaction, proving the failure was transient and fixed automatically.
Pre-deployment schema validation
A Platform Architect is setting up a new hybrid database template. They run list_blueprints to confirm the base template exists, then use get_blueprint to pull its structural properties before committing code.
Inventorying cloud capabilities
A developer joining the team needs to know what services are connected. They ask their agent to run list_plugins, getting a list of all available Python abstractions for GCP and AWS in seconds.
Troubleshooting resource visibility
The DevOps engineer sees an error but doesn't know which specific node is broken. They use list_nodes to get the exact name and status of all instances related to the failing service, pinpointing the issue immediately.
The honest tradeoffs
Checking status via multiple manual steps
The user logs into the manager, clicks 'Deployments,' finds the ID, then navigates to 'Executions' and copies the ID again for comparison.
Just ask your agent. The agent uses list_deployments followed by get_deployment to retrieve all necessary structural state data in two clean steps.
Forgetting cloud context
The user asks for 'all nodes' but doesn't specify the region or cloud provider, resulting in a massive, unreadable list of resources.
Always be specific. To check only AWS nodes, ask your agent to list_nodes and filter by the known resource group name.
Over-relying on UI filtering
Trying to find a plugin but getting lost in dropdown menus that require multiple clicks and assumptions about naming conventions.
Use list_plugins. This tool directly pulls the explicit capabilities map, showing you what's available for AWS or GCP right now.
When It Fits, When It Doesn't
Use this MCP if your job involves managing complex infrastructure where state visibility is critical. You need to know what was deployed and if it’s working—not just that a process ran. If you only ever need to check simple, isolated service statuses (e.g., 'Is Service X up?'), the raw API might suffice. However, if your work involves auditing multi-cloud compliance, checking blueprints, or tracing complex deployment histories across different lifecycle stages, this MCP is essential. Don't try to use it just because you can; use it when manual cross-referencing takes longer than five minutes.
Questions you might have
How do I check active deployments using list_deployments? +
Run list_deployments to retrieve the structural match and verify all actualized runtime schemas. This gives you a definitive, up-to-date view of everything currently deployed.
What is the difference between list_nodes and get_deployment? +
list_nodes identifies specific instances (literal limits) for auditing lifecycle properties. get_deployment pulls the broader, explicit structural states for an entire execution topology.
Can I audit my cloud plugins using list_plugins? +
Yes. Use list_plugins to extract a map of all installed Python abstractions for various cloud integrations like AWS and GCP.
Does list_executions show me failed deployments? +
Yes, it identifies precise active cluster limits spanning deployment workflow bounds. This allows you to see the history, including any 'heal' or 'uninstall' events that occurred.
What specific properties can I extract about a blueprint schema using get_blueprint? +
It pulls the structural definition of your blueprints. You'll get details on the properties that drive active schemas, allowing you to verify the template structure before deploying anything.
How does running get_deployment provide a deeper view than just list_deployments? +
While list_deployments gives you the names of active deployments, get_deployment extracts the precise internal structural states. This shows the full execution topology for that specific deployment.
When using list_nodes, what lifecycle properties can I audit? +
list_nodes lets you check the complete lifecycle status of your infrastructure nodes. You can see if a node is started, created, or deleted, helping you track its exact current state.
What kinds of cloud integrations are available using list_plugins? +
list_plugins finds the installed Python abstractions for various clouds. It confirms which major services, like AWS and GCP, your infrastructure can connect to right out of the box.
Can my agent list all active cloud deployments? +
Yes. Use the 'list_deployments' tool. Your agent will retrieve the exact structural matching of your actualized runtime schemas, showing you every environment currently managed by Cloudify.
How do I check the lifecycle state of a specific infrastructure node? +
Provide the deployment ID to your agent and use the 'list_nodes' tool. The agent will resolve deeply nested nodes and identify whether instances are in 'started', 'created', or 'deleted' states.
Can I monitor pending workflow executions through the agent? +
Absolutely. The 'list_executions' tool surfaces active mapping for install, uninstall, and heal workflows. This allows you to track transactions and deployment events strictly within Cloudify limits.
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