How to Use the Cloudify MCP in CrewAI

Q: How do I pass this Cloudify MCP Server to a CrewAI agent?

Add your Vinkius endpoint URL directly to the agent's mcps list during initialization. CrewAI automatically discovers all seven tools and makes them available to the agent.

Q: Can I restrict which Cloudify tools a CrewAI agent can use?

Yes, use the MCPServerHTTP class from the CrewAI MCP library and apply a toolfilter. This lets you restrict access to sensitive tools like getblueprint while allowing monitoring tools.

Q: Does this setup support sequential execution for cloud tasks?

Yes, you can design a CrewAI pipeline where Agent A runs listexecutions, and if it finds an error, Agent B is triggered to inspect the specific nodes using listnodes.

Run autonomous multi-agent teams in CrewAI to monitor, analyze, and scale your Cloudify deployments.

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Connect Cloudify MCP to CrewAI

Create your Vinkius account to connect Cloudify to CrewAI and route execution through our secure gateway. The platform manages server hosting, runtime updates, and security layers. Configuration requires no manual server provisioning.

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Setup Cloudify with CrewAI

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Deploy multi-agent crews for Cloudify monitoring

The `list_executions` tool allows your monitoring agent to track active cluster limits and workflow boundaries using the MCP. While this agent watches the execution state, a separate moderator agent stands ready to act if a step hangs. CrewAI's shared memory lets these agents collaborate on complex problems without losing context. If the monitoring agent spots a bottleneck, it hands the execution ID to the analyst agent to dig deeper.

Analyze topology anomalies with specialized agents

The `list_nodes` tool feeds exact literal instances and deployment routing rules into your CrewAI analysis loop. Your specialist agents divide the work: one maps the active nodes while another checks the core blueprint properties. By calling `get_deployment`, the crew extracts the precise internal structural states of your running topology. The agents compare this live state against the target blueprint to flag drift before it breaks your production stack.

Validate blueprints using CrewAI agent teams

The `list_blueprints` tool identifies bounded logical arrays managing your top-level deployment schemas. Your CrewAI developer agent inspects these schemas to ensure they comply with your team's internal security policies. The agent then queries `list_plugins` to check the active cluster supports the native deployment limits required by the blueprint. This cooperative check process ensures only compliant configurations reach your production environment.

Setup guide

Set up Cloudify MCP in CrewAI

Prerequisites

Python 3.10+ installed
crewai package (pip install crewai)
Active Vinkius subscription with a valid endpoint token

1

Install CrewAI
Run pip install crewai to install the framework. MCP support is built-in via the mcps parameter.
2

Add the MCP URL to your agent
Pass your Vinkius endpoint directly to the mcps list. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. CrewAI handles tool discovery and caching automatically.
3

Kick off your crew
Create a Crew with your agent and tasks. Call crew.kickoff() — the agent will automatically invoke Cloudify tools as needed.

crew.py

from crewai import Agent, Task, Crew

agent = Agent(
    role="Cloudify Analyst",
    goal="Access and analyze Cloudify data via MCP.",
    backstory="Expert analyst with direct Cloudify access.",
    mcps=[
        "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
    ],
)

task = Task(
    description="List recent Cloudify transactions",
    agent=agent,
    expected_output="A summary of recent activity",
)

crew = Crew(agents=[agent], tasks=[task])
result = crew.kickoff()
print(result)

Get your connection token →

Prerequisites

Python 3.10+ installed
crewai + crewai-tools packages
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install crewai crewai-tools. The MCPServerAdapter handles lifecycle management and tool conversion.
2

Connect with MCPServerAdapter
Use MCPServerAdapter as a context manager with SseServerParameters pointing to your Vinkius endpoint. The adapter automatically manages connection lifecycle.
3

Assign tools and run
Pass the returned mcp_tools to your agent's tools parameter. The adapter converts MCP tools to native BaseTool objects compatible with all CrewAI agents.

crew.py

from crewai import Agent, Task, Crew
from crewai_tools import MCPServerAdapter
from mcp import SseServerParameters

server_params = SseServerParameters(
    url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)

with MCPServerAdapter(server_params) as mcp_tools:
    agent = Agent(
        role="Cloudify Analyst",
        goal="Access and analyze Cloudify data via MCP.",
        backstory="Expert analyst with direct Cloudify access.",
        tools=mcp_tools,
    )

    task = Task(
        description="List recent Cloudify transactions",
        agent=agent,
        expected_output="A summary of recent activity",
    )

    crew = Crew(agents=[agent], tasks=[task])
    result = crew.kickoff()
    print(result)

Get your connection token →

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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Common questions about Cloudify MCP in CrewAI

Add your Vinkius endpoint URL directly to the agent's `mcps` list during initialization. CrewAI automatically discovers all seven tools and makes them available to the agent.

Yes, use the `MCPServerHTTP` class from the CrewAI MCP library and apply a `tool_filter`. This lets you restrict access to sensitive tools like `get_blueprint` while allowing monitoring tools.

Agents use CrewAI's shared memory to pass output from tools like `get_deployment` to each other. The research agent pulls the topology data using the MCP, and the analyst agent reviews it.

Yes, you can design a CrewAI pipeline where Agent A runs `list_executions`, and if it finds an error, Agent B is triggered to inspect the specific nodes using `list_nodes`.

All execution metrics and node states are fetched securely via the Vinkius MCP Server. The data is processed directly inside your local CrewAI Python runtime and never stored externally.

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