How to Use the Cloudify MCP in Google ADK

Q: Does Google ADK support Cloudify tool filtering?

You can use the toolnames filter to restrict what the agent sees. This keeps your agent focused on specific deployment tasks.

Connect Cloudify to your Google ADK agents for deep infrastructure reasoning.

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Connect Cloudify MCP to Google ADK

Create your Vinkius account to connect Cloudify to Google ADK 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 Google ADK

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Map orchestration with Google ADK

Call `list_blueprints` to identify your top-level orchestration schemas. Your agent uses these lists to understand the full scope of your cloud setup. This turns your infrastructure into a searchable index. Your agent can now reason about your blueprints alongside your BigQuery data.

Verify runtime schemas

Use `list_deployments` to get the exact structural matching for your runtime environment. Your agent checks this against your current logic. This ensures your agent acts on the right cluster. It removes the ambiguity that usually comes with managing live deployments.

Audit active node instances

Run `list_nodes` to identify the literal limits pushing specific instances. Your agent sees how the rules are applied to each machine. This is essential for debugging. Your agent identifies which nodes are causing bottlenecks by looking at the raw orchestration rules.

Setup guide

Set up Cloudify MCP in Google ADK

Prerequisites

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

1

Install Google ADK

Run pip install google-adk to install the Agent Development Kit. MCP support is included via the McpToolset class.
2

Connect via SSE transport

Use McpToolset.from_server() with SseServerParams pointing to your Vinkius endpoint. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Create an LlmAgent

Pass the returned mcp_tools list directly to LlmAgent(tools=mcp_tools). The ADK maps each MCP tool to a native Gemini function call — no manual schema definitions required.
4

Run with any Gemini model

The agent works with any Gemini model (gemini-2.0-flash, gemini-2.5-pro, etc.). Copy the full example on the right to get started with Cloudify tools in your ADK agent.

agent.py

from google.adk.agents import LlmAgent
from google.adk.tools.mcp_tool.mcp_toolset import McpToolset
from google.adk.tools.mcp_tool.mcp_session_manager import SseServerParams

# Connect to the MCP via SSE
mcp_tools, exit_stack = await McpToolset.from_server(
    connection_params=SseServerParams(
        url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
    )
)

# Create your agent with auto-discovered tools
agent = LlmAgent(
    name="Cloudify_agent",
    model="gemini-2.0-flash",
    instruction="You have access to Cloudify tools via MCP.",
    tools=mcp_tools,
)

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Common questions about Cloudify MCP in Google ADK

Use the McpToolset class in your Python file. Pass your server params to the toolset and include it in your LlmAgent definition.

You can use the tool_names filter to restrict what the agent sees. This keeps your agent focused on specific deployment tasks.

Your agent can combine data from both sources easily. It reads your Cloudify deployment state and correlates it with your Google Cloud metrics.

Yes, it handles both Stdio and HTTP. You pick the transport that fits your environment architecture.

Vinkius isolates your session completely. Only your agent interacts with the blueprint and node data, keeping your private environment details hidden from other processes.

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