Cloudify MCP Server for LlamaIndex 7 tools — connect in under 2 minutes
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add Cloudify as an MCP tool provider through Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.
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Vinkius supports streamable HTTP and SSE.
import asyncio
from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI
async def main():
# Your Vinkius token. get it at cloud.vinkius.com
mcp_client = BasicMCPClient("https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")
mcp_tool_spec = McpToolSpec(client=mcp_client)
tools = await mcp_tool_spec.to_tool_list_async()
agent = FunctionAgent(
tools=tools,
llm=OpenAI(model="gpt-4o"),
system_prompt=(
"You are an assistant with access to Cloudify. "
"You have 7 tools available."
),
)
response = await agent.run(
"What tools are available in Cloudify?"
)
print(response)
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.
LlamaIndex agents combine Cloudify tool responses with indexed documents for comprehensive, grounded answers. Connect 7 tools through Vinkius and query live data alongside vector stores and SQL databases in a single turn. ideal for hybrid search, data enrichment, and analytical workflows.
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 LlamaIndex 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 LlamaIndex via MCP
Follow these steps to integrate the Cloudify MCP Server with LlamaIndex.
Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai
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
Why Use LlamaIndex with the Cloudify MCP Server
LlamaIndex provides unique advantages when paired with Cloudify through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine Cloudify tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain Cloudify tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query Cloudify, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what Cloudify tools were called, what data was returned, and how it influenced the final answer
Cloudify + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the Cloudify MCP Server delivers measurable value.
Hybrid search: combine Cloudify real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query Cloudify to augment indexed data with live information before generating user-facing responses
Knowledge base agents: build agents that maintain and update knowledge bases by periodically querying Cloudify for fresh data
Analytical workflows: chain Cloudify queries with LlamaIndex's data connectors to build multi-source analytical reports
Cloudify MCP Tools for LlamaIndex (7)
These 7 tools become available when you connect Cloudify to LlamaIndex 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 LlamaIndex
Ready-to-use prompts you can give your LlamaIndex 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 LlamaIndex
Common issues when connecting Cloudify to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpCloudify + LlamaIndex FAQ
Common questions about integrating Cloudify MCP Server with LlamaIndex.
How does LlamaIndex connect to MCP servers?
Can I combine MCP tools with vector stores?
Does LlamaIndex support async MCP calls?
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 LlamaIndex
Get your token, paste the configuration, and start using 7 tools in under 2 minutes. No API key management needed.