Cloudify MCP Server for CrewAI 7 tools — connect in under 2 minutes
Connect your CrewAI agents to Cloudify through Vinkius, pass the Edge URL in the `mcps` parameter and every Cloudify tool is auto-discovered at runtime. No credentials to manage, no infrastructure to maintain.
ASK AI ABOUT THIS MCP SERVER
Vinkius supports streamable HTTP and SSE.
from crewai import Agent, Task, Crew
agent = Agent(
role="Cloudify Specialist",
goal="Help users interact with Cloudify effectively",
backstory=(
"You are an expert at leveraging Cloudify 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 Cloudify "
"and summarize their capabilities."
),
agent=agent,
expected_output=(
"A detailed summary of 7 available tools "
"and what they can do."
),
)
crew = Crew(agents=[agent], tasks=[task])
result = crew.kickoff()
print(result)
* 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 Cloudify MCP Server
Connect your Cloudify Manager to any AI agent and take full control of your multi-cloud orchestration through natural conversation.
When paired with CrewAI, Cloudify becomes a first-class tool in your multi-agent workflows. Each agent in the crew can call Cloudify tools autonomously, one agent queries data, another analyzes results, a third compiles reports, all orchestrated through Vinkius with zero configuration overhead.
What you can do
- Blueprint Management — List and audit OASIS TOSCA blueprints parsing root Cloudify manager templates
- Deployment Tracking — Retrieve exact structural matching of actualized runtime schemas and manage infrastructure states
- Workflow Executions — Monitor install, uninstall, and heal transactions to track deployment events in real-time
- Node Inspections — Resolve deeply nested infrastructure nodes and audit lifecycle properties (started, created, deleted)
- Plugin Auditing — Discover installed Python abstractions for AWS, GCP, and other cloud integrations
The Cloudify MCP Server exposes 7 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 Cloudify to CrewAI via MCP
Follow these steps to integrate the Cloudify 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 7 tools from Cloudify
Why Use CrewAI with the Cloudify MCP Server
CrewAI Multi-Agent Orchestration Framework provides unique advantages when paired with Cloudify 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 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
Cloudify + CrewAI Use Cases
Practical scenarios where CrewAI combined with the Cloudify MCP Server delivers measurable value.
Automated multi-step research: a reconnaissance agent queries Cloudify 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 Cloudify, analyzes trends over time, and generates executive briefings in markdown or PDF format
Multi-source enrichment pipelines: chain Cloudify 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 Cloudify against predefined policy rules, generates deviation reports, and routes findings to the appropriate team
Cloudify MCP Tools for CrewAI (7)
These 7 tools become available when you connect Cloudify to CrewAI via MCP:
get_blueprint
Perform structural extraction of properties driving active blueprint schemas
get_deployment
Extracts explicitly attached internal structural states pulling precise execution topologies
list_blueprints
Identify bounded logical arrays managing top-level orchestration schemas
list_deployments
Retrieve the exact structural matching verifying actualized runtime schemas
list_executions
Identify precise active cluster limits spanning deployment workflow bounds
list_nodes
Identify exact literal limits pushing specific instances routing orchestration rules
list_plugins
Extracts explicit capabilities mapping native orchestration limits
Example Prompts for Cloudify in CrewAI
Ready-to-use prompts you can give your CrewAI agent to start working with Cloudify immediately.
"List all blueprints in Cloudify Manager"
"Show me the execution history for deployment 'web-app-prod'"
"What nodes are currently in the 'started' state for deployment 'db-cluster'?"
Troubleshooting Cloudify MCP Server with CrewAI
Common issues when connecting Cloudify to CrewAI through the Vinkius, and how to resolve them.
MCP tools not discovered
Agent not using tools
Timeout errors
Rate limiting or 429 errors
Cloudify + CrewAI FAQ
Common questions about integrating Cloudify MCP Server with CrewAI.
How does CrewAI discover and connect to MCP tools?
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?
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?
Can CrewAI agents call multiple MCP tools in parallel?
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)?
crew.kickoff() method runs synchronously by default, making it straightforward to integrate into existing pipelines.Connect Cloudify with your favorite client
Step-by-step setup guides for every MCP-compatible client and framework:
Anthropic's native desktop app for Claude with built-in MCP support.
AI-first code editor with integrated LLM-powered coding assistance.
GitHub Copilot in VS Code with Agent mode and MCP support.
Purpose-built IDE for agentic AI coding workflows.
Autonomous AI coding agent that runs inside VS Code.
Anthropic's agentic CLI for terminal-first development.
Python SDK for building production-grade OpenAI agent workflows.
Google's framework for building production AI agents.
Type-safe agent development for Python with first-class MCP support.
TypeScript toolkit for building AI-powered web applications.
TypeScript-native agent framework for modern web stacks.
Python framework for orchestrating collaborative AI agent crews.
Leading Python framework for composable LLM applications.
Data-aware AI agent framework for structured and unstructured sources.
Microsoft's framework for multi-agent collaborative conversations.
Connect Cloudify to CrewAI
Get your token, paste the configuration, and start using 7 tools in under 2 minutes. No API key management needed.