How to Use the Cloudify MCP in Pydantic AI

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Connect Cloudify MCP to Pydantic AI

Create your Vinkius account to connect Cloudify to Pydantic AI 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 Pydantic AI

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Claude

Perplexity

Validate orchestrator plugins

Use `list_plugins` to extract capabilities and map them to your Pydantic models. Every response is checked against your schema definitions. This prevents your agent from processing garbage data. If the server returns an unexpected format, your code throws a validation error immediately.

Extract precise execution topologies

Call `get_deployment` to pull internal structural states into your agent. Pydantic AI ensures the data matches your expected types. This makes your logic bulletproof. You don't have to write manual checks for every field returned by the MCP server.

Manage deployment workflow bounds

Run `list_executions` to see active cluster limits. Your agent reads these as strongly-typed objects. This stops silent corruption in its tracks. Your agent won't act on a workflow execution if the data structure changes unexpectedly.

Setup guide

Set up Cloudify MCP in Pydantic AI

Prerequisites

Python 3.10+ installed
pydantic-ai-slim[fastmcp] package
Active Vinkius subscription with a valid endpoint token

1

Install Pydantic AI with FastMCP
Run pip install "pydantic-ai-slim[fastmcp]". The FastMCP toolset replaces the deprecated MCPServerHTTP class with full protocol support.
2

Configure the FastMCPToolset
Pass a JSON-style config dict to FastMCPToolset with your Vinkius URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. Supports Streamable HTTP, SSE, and Stdio transports.
3

Create and run your agent
Pass the toolset to Agent(toolsets=[toolset]) and call agent.run(). Swap openai:gpt-4o for any supported model — Anthropic, Google, Mistral, or Groq.

agent.py

from pydantic_ai import Agent
from pydantic_ai.toolsets.fastmcp import FastMCPToolset

toolset = FastMCPToolset({
    "mcpServers": {
        "cloudify-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

agent = Agent(
    "openai:gpt-4o",
    toolsets=[toolset],
    system_prompt="You have access to Cloudify tools.",
)

result = await agent.run("List recent Cloudify transactions")
print(result.output)

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Common questions about Cloudify MCP in Pydantic AI

Install the slim package and use MCPToolset to point at your server. Pass the toolset into your Agent constructor for full type safety.

Every tool response is validated against your models. If the server returns a field that doesn't fit, the agent fails before it acts.

Yes, the toolset supports both Streamable HTTP and SSE. You just need the server running at the provided endpoint.

It is fully compatible. The toolset handles the conversion between the MCP protocol and your typed Python models.

Vinkius uses an ephemeral sandbox for your connection. Your deployment topology and blueprint data are encrypted in transit and never stored outside your active session.

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