Cloudify MCP. Control multi-cloud blueprints and deployments via chat.
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Cloudify MCP Server lets you manage complex multi-cloud infrastructure. Use your AI agent to list blueprints, track deployments, audit node states, and monitor workflow executions across AWS, GCP, and other platforms.
It gives you full, conversational control over your entire cloud stack without needing to navigate the Cloudify Manager UI.
What your AI agents can do
Get blueprint
Extracts properties defining the structure of an active blueprint schema.
Get deployment
Pulls the specific internal structural states of a deployment, showing the current execution topology.
List blueprints
Identifies and lists the top-level orchestration blueprints available in the system.
Lists and reads the properties of defined TOSCA blueprints within the Cloudify Manager.
Retrieves the exact structural states of running deployments, giving you a view of the current infrastructure topology.
Tracks the full lifecycle of a deployment, including install, uninstall, and healing transactions.
Resolves individual, deeply nested infrastructure components and reports on their current lifecycle status.
Lists and audits the Python abstractions installed for various cloud providers like AWS and GCP.
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Supported MCP Clients
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Cloudify MCP Server: 7 Tools for Infrastructure Management
These seven tools allow your agent to interact with the Cloudify Manager API, enabling you to audit blueprints, track deployments, and inspect every node in your cloud infrastructure.
019d7574get blueprint
Extracts properties defining the structure of an active blueprint schema.
019d7574get deployment
Pulls the specific internal structural states of a deployment, showing the current execution topology.
019d7574list blueprints
Identifies and lists the top-level orchestration blueprints available in the system.
019d7574list deployments
Retrieves the structural list of all active deployments and their runtime schemas.
019d7574list executions
Identifies and lists active cluster limits and workflow boundaries for deployments.
019d7574list nodes
Identifies and lists specific instances that are routing orchestration rules.
019d7574list plugins
Extracts and lists the explicit capabilities available for 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.
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- Works with Claude, ChatGPT, Cursor, and more
- New servers added to the catalog every week
What you can do with this MCP connector
Cloudify MCP Server - Manage Multi-Cloud Deployments
Manually managing a multi-cloud stack is a nightmare. This server lets your AI agent talk directly to your Cloudify Manager, giving you full conversational control over your entire cloud infrastructure. You don't gotta click through the Cloudify Manager UI; your agent handles the heavy lifting.
Audit Blueprints
list_blueprintslets you see every top-level orchestration blueprint available in the system.get_blueprintpulls the specific properties defining the structure of an active blueprint schema.
Check Deployment States
list_deploymentspulls a structural list of all active deployments and their runtime schemas.get_deploymentgets the specific internal structural states of a deployment, showing you the current execution topology.list_executionsidentifies and lists active cluster limits and workflow boundaries for deployments.list_nodesidentifies and lists specific instances that are routing orchestration rules.get_deploymentgives you the structural states of a running deployment, so you know exactly what's running right now.
Discover Cloud Plugins
list_pluginsextracts and lists the explicit capabilities available for cloud integrations, like AWS and GCP abstractions.
Monitor Infrastructure Nodes and Workflows
list_nodeshelps you find specific infrastructure components that are routing orchestration rules.get_deploymentshows you the full structural state, letting you track the deployment's lifecycle.
Basically, your agent lets you list blueprints, track deployments, audit node states, and monitor workflow executions across AWS, GCP, and other platforms.
How Cloudify MCP Works
- 1 Subscribe to the server and enter your Cloudify Manager URL and API Token.
- 2 Your AI client sends a natural language request (e.g., 'Show me the status of the staging database').
- 3 The server executes the relevant tool, retrieves the structured data, and sends it back to your AI client for a conversational summary.
The bottom line is you manage complex, multi-cloud infrastructure by talking to your AI client instead of clicking through dashboards.
Who Is Cloudify MCP For?
Cloud Engineers, DevOps Teams, and Platform Architects use this when they need visibility into complex, multi-cloud deployments. It’s for the ops engineer who can't afford to spend hours digging through the Cloudify Manager UI just to confirm a node state or check a blueprint version.
Uses the server to audit complex orchestration blueprints and manage deployment lifecycles using only natural language commands.
Monitors workflow executions and node states across multiple environments without opening the manager UI.
Audits multi-cloud integrations and plugin configurations across different environments to ensure governance.
What Changes When You Connect
- Real-time monitoring: Instead of digging through the manager UI, use
list_executionsto track deployment events in real-time. You instantly know if an install or heal transaction succeeded or failed. - Full lifecycle visibility: Need to know what's running? Run
list_deploymentsto get the structural list of all active deployments and verify their actualized runtime schemas. - Deep audit capability: Use
list_nodesto resolve deeply nested infrastructure components. You can audit the lifecycle properties (started, created, deleted) of any specific node. - Blueprint comparison: Want to see what's possible? Use
list_blueprintsto list all top-level blueprints, and thenget_blueprintto pull the specific structural properties for comparison. - Cloud integration audit: Don't trust a black box. Use
list_pluginsto discover every installed Python abstraction for AWS, GCP, and other cloud integrations, verifying your entire tech stack. - State management: Use
get_deploymentto pull the precise execution topologies, letting you confirm the exact structural state without guesswork.
Real-World Use Cases
Debugging a Failed Staging Deploy
A DevOps team member notices the staging environment deployment is stuck. Instead of logging into the manager UI and clicking through tabs, they ask their agent: 'Check the execution history for the staging deployment.' The agent runs list_executions and provides the exact failure point and the last successful workflow state, letting them fix it immediately.
Validating a New Cloud Integration
A Platform Architect needs to confirm if GCP support is available. They ask their agent to 'List all installed cloud plugins.' The agent runs list_plugins, immediately listing all available Python abstractions for AWS, GCP, and other clouds, confirming the integration point without manual searching.
Auditing Infrastructure Changes
The Cloud Engineer needs to know the structure of the core microservice blueprint. They ask the agent to 'Show the blueprint structure for the microservice.' The agent runs get_blueprint, giving them the raw, actionable structural properties, which is crucial for compliance audits.
Confirming Node Status in Production
An SRE needs to verify if a database cluster is fully initialized. They ask the agent to 'List all nodes in the primary cluster.' The agent runs list_nodes, giving a list of every instance and its current lifecycle status, confirming all required nodes are 'started' and healthy.
The Tradeoffs
Treating the server like a simple data lookup
Trying to ask the agent, 'What is the status?' without specifying what you are looking for. The agent gets confused because 'status' applies to nodes, deployments, and blueprints.
→
Be specific. Always start with the scope. For instance, ask: 'What is the current structural state of the 'web-app-prod' deployment?' This directs the agent to run get_deployment or list_deployments.
Manually chaining API calls in a script
Writing a script that first calls list_blueprints, then loops through results, and then calls get_blueprint for each one. This is brittle and fails if the underlying API structure changes.
→
Let your AI agent handle the sequence. Instead of writing the loop, ask your agent: 'Get the blueprints for all environments and list their properties.' The agent handles the list_blueprints -> get_blueprint sequence for you.
Assuming a single tool does everything
Thinking that just calling list_nodes will show you the deployment's health. It only shows the nodes, not the overall transaction status.
→
Check the workflow status first. If you need deployment status, run list_executions to see the transaction boundaries. Then, if you need node details, run list_nodes.
When It Fits, When It Doesn't
Use this MCP Server if your job requires constant, deep visibility into multi-cloud infrastructure state. You need to know why something failed—was it the blueprint definition, the node lifecycle, or the deployment workflow itself? Use it to audit and track state across different cloud providers (AWS, GCP, etc.).
Don't use it if you just need a simple list of names or basic metrics. If all you need is 'Are there 5 blueprints?', you could use a simpler list tool. But if you need the details (the properties, the schemas, the nodes), this server is necessary. It's designed for deep platform operations, not quick lookups.
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.
Available Capabilities
Checking cloud infrastructure status used to mean clicking 10 different tabs.
Used to check a deployment's status, you'd log into the Cloudify Manager. You'd navigate to the deployment, check the history tab, then click on the node list, and maybe cross-reference a blueprint version in a sidebar. It was a painful, manual process of jumping between tabs and copy-pasting IDs.
Now, you just tell your agent: 'What's the status of the staging deployment?' It runs `list_executions` and immediately gives you the full, conversational history, pointing out the exact failure point. You get the answer in one chat message.
Cloudify MCP Server: Use `list_plugins` to audit your integrations.
Before, auditing integrations meant downloading reports or digging into specific sections of the manager UI, hoping you didn't miss a plugin abstraction for a specific cloud service. It was slow, tedious, and often incomplete.
Now, you ask the agent to 'List all installed plugins.' The agent runs `list_plugins` and hands you a complete, programmatic list of every abstraction for AWS, GCP, and other clouds. You get a definitive, auditable list in seconds.
Common Questions About Cloudify MCP
How do I use the `list_blueprints` tool in Cloudify MCP Server? +
The list_blueprints tool lists all top-level blueprints available in your Cloudify Manager. You use this when you need to know what templates exist before you can inspect a specific one using get_blueprint.
Can I use `list_nodes` to check the health of a deployment? +
No, list_nodes only identifies and lists specific infrastructure components. To check overall deployment health, you need to run list_executions to see the transaction status.
What is the difference between `get_deployment` and `list_deployments`? +
list_deployments gives you the list of all deployed services. get_deployment drills into one specific deployment to pull its precise, current structural state and topology.
How do I check if a blueprint was updated? +
You first use list_blueprints to see all available templates. Then, you use get_blueprint and provide the specific blueprint ID to pull its current structural properties for comparison.
Does Cloudify MCP Server handle multi-cloud environments? +
Yes. The server is designed for multi-cloud orchestration, allowing you to audit and manage infrastructure across multiple platforms like AWS and GCP from a single interface.
How do I use `list_plugins` to check what cloud integrations are available? +
It lists all installed Python abstractions. This tells you exactly which cloud platforms—like AWS or GCP—are configured for use with your blueprints.
What should I use to track a specific deployment's runtime state: `get_deployment` or `list_deployments`? +
get_deployment pulls the specific, internal structural states. Use this when you need the exact topology of one running execution, while list_deployments just gives you a list of all existing ones.
If a workflow fails, how can I use `list_executions` to find the failure details? +
It identifies the precise active cluster limits and boundaries for a given deployment. You can pinpoint the failure event and check the logs associated with that specific execution time.
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.
Use it with your favorite AI tools
Connect this server to Cursor, Claude, VS Code, and more.
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