Cloudify MCP Server for Pydantic AI 7 tools — connect in under 2 minutes
Pydantic AI brings type-safe agent development to Python with first-class MCP support. Connect Cloudify through Vinkius and every tool is automatically validated against Pydantic schemas. catch errors at build time, not in production.
ASK AI ABOUT THIS MCP SERVER
Vinkius supports streamable HTTP and SSE.
import asyncio
from pydantic_ai import Agent
from pydantic_ai.mcp import MCPServerHTTP
async def main():
# Your Vinkius token. get it at cloud.vinkius.com
server = MCPServerHTTP(url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")
agent = Agent(
model="openai:gpt-4o",
mcp_servers=[server],
system_prompt=(
"You are an assistant with access to Cloudify "
"(7 tools)."
),
)
result = await agent.run(
"What tools are available in Cloudify?"
)
print(result.data)
asyncio.run(main())
* 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.
Pydantic AI validates every Cloudify tool response against typed schemas, catching data inconsistencies at build time. Connect 7 tools through Vinkius and switch between OpenAI, Anthropic, or Gemini without changing your integration code. full type safety, structured output guarantees, and dependency injection for testable agents.
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 Pydantic AI 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 Pydantic AI via MCP
Follow these steps to integrate the Cloudify MCP Server with Pydantic AI.
Install Pydantic AI
Run pip install pydantic-ai
Replace the token
Replace [YOUR_TOKEN_HERE] with your Vinkius token
Run the agent
Save to agent.py and run: python agent.py
Explore tools
The agent discovers 7 tools from Cloudify with type-safe schemas
Why Use Pydantic AI with the Cloudify MCP Server
Pydantic AI provides unique advantages when paired with Cloudify through the Model Context Protocol.
Full type safety: every MCP tool response is validated against Pydantic models, catching data inconsistencies before they reach your application
Model-agnostic architecture. switch between OpenAI, Anthropic, or Gemini without changing your Cloudify integration code
Structured output guarantee: Pydantic AI ensures tool results conform to defined schemas, eliminating runtime type errors
Dependency injection system cleanly separates your Cloudify connection logic from agent behavior for testable, maintainable code
Cloudify + Pydantic AI Use Cases
Practical scenarios where Pydantic AI combined with the Cloudify MCP Server delivers measurable value.
Type-safe data pipelines: query Cloudify with guaranteed response schemas, feeding validated data into downstream processing
API orchestration: chain multiple Cloudify tool calls with Pydantic validation at each step to ensure data integrity end-to-end
Production monitoring: build validated alert agents that query Cloudify and output structured, schema-compliant notifications
Testing and QA: use Pydantic AI's dependency injection to mock Cloudify responses and write comprehensive agent tests
Cloudify MCP Tools for Pydantic AI (7)
These 7 tools become available when you connect Cloudify to Pydantic AI 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 Pydantic AI
Ready-to-use prompts you can give your Pydantic AI 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 Pydantic AI
Common issues when connecting Cloudify to Pydantic AI through the Vinkius, and how to resolve them.
MCPServerHTTP not found
pip install --upgrade pydantic-aiCloudify + Pydantic AI FAQ
Common questions about integrating Cloudify MCP Server with Pydantic AI.
How does Pydantic AI discover MCP tools?
MCPServerHTTP instance with the server URL. Pydantic AI connects, discovers all tools, and generates typed Python interfaces automatically.Does Pydantic AI validate MCP tool responses?
Can I switch LLM providers without changing MCP code?
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 Pydantic AI
Get your token, paste the configuration, and start using 7 tools in under 2 minutes. No API key management needed.