Pulumi MCP Server for LlamaIndex 11 tools — connect in under 2 minutes
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add Pulumi as an MCP tool provider through the Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.
ASK AI ABOUT THIS MCP SERVER
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 Pulumi. "
"You have 11 tools available."
),
)
response = await agent.run(
"What tools are available in Pulumi?"
)
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 Pulumi MCP Server
Connect your Pulumi account to any AI agent and take full control of your infrastructure-as-code through natural conversation.
LlamaIndex agents combine Pulumi tool responses with indexed documents for comprehensive, grounded answers. Connect 11 tools through the 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
- Organization Discovery — List organizations and retrieve their details, team settings and member info
- Stack Management — List, create and delete stacks (infrastructure environments) across all your projects
- Deployment Tracking — Monitor stack update history with status (succeeded, failed, in-progress), resource changes and error logs
- Output Inspection — View exported output values from the latest deployment (URLs, IPs, resource IDs)
- Tag Management — List and set custom tags on stacks for organization and filtering (environment, team, cost-center)
The Pulumi MCP Server exposes 11 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 Pulumi to LlamaIndex via MCP
Follow these steps to integrate the Pulumi 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 11 tools from Pulumi
Why Use LlamaIndex with the Pulumi MCP Server
LlamaIndex provides unique advantages when paired with Pulumi through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine Pulumi tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain Pulumi tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query Pulumi, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what Pulumi tools were called, what data was returned, and how it influenced the final answer
Pulumi + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the Pulumi MCP Server delivers measurable value.
Hybrid search: combine Pulumi real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query Pulumi 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 Pulumi for fresh data
Analytical workflows: chain Pulumi queries with LlamaIndex's data connectors to build multi-source analytical reports
Pulumi MCP Tools for LlamaIndex (11)
These 11 tools become available when you connect Pulumi to LlamaIndex via MCP:
create_stack
A stack is an isolated, independently configurable instance of your Pulumi program. Requires the org name, project name and stack name (e.g. "staging", "prod"). Returns the created stack with its URL. Create a new Pulumi stack
delete_stack
The stack must be empty (no resources) or force deletion must be enabled. Provide the org name, project name and stack name. WARNING: this action is irreversible. Delete a Pulumi stack
get_current_user
Returns the user's GitHub login, avatar URL, email and name. Use this to verify your access token is working correctly and to see which identity the API calls will appear as. Get the currently authenticated Pulumi user
get_deployment
Provide the org name, project name, stack name and deployment version number. Get details for a specific Pulumi deployment
get_organization
Provide the organization name (slug). Get details for a specific Pulumi organization
get_stack
Provide the org name, project name and stack name. Get details for a specific Pulumi stack
get_stack_outputs
Outputs are values your Pulumi program exports, such as URLs, IP addresses, resource IDs and connection strings. Useful for discovering endpoint addresses and configuration values after infrastructure deployment. Get the exported output values from a Pulumi stack
list_deployments
Each deployment shows its version number, status (succeeded, failed, in-progress), start/end time, resource changes (created, updated, deleted) and the user who triggered it. Use this to audit infrastructure changes and track deployment success/failure patterns. List deployment history for a Pulumi stack
list_stack_tags
Tags are key-value metadata labels used for organizing, filtering and managing stacks (e.g. environment=prod, team=platform, cost-center=engineering). List tags on a Pulumi stack
list_stacks
Each stack represents an isolated, independently configurable instance of your infrastructure (e.g. dev, staging, prod). Returns stack name, project name, last update info, resource count and whether updates are in progress. List all stacks in a Pulumi organization
set_stack_tag
Tags are used for organizing, filtering and managing stacks (e.g. key="environment", value="prod", key="team", value="platform"). Provide the org name, project name, stack name, tag name and tag value. Set a tag on a Pulumi stack
Example Prompts for Pulumi in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with Pulumi immediately.
"Show me all stacks in my organization."
"What was the result of the latest deployment to my-infra/prod?"
"Show me the exported outputs from the prod stack."
Troubleshooting Pulumi MCP Server with LlamaIndex
Common issues when connecting Pulumi to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpPulumi + LlamaIndex FAQ
Common questions about integrating Pulumi 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 Pulumi 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 Pulumi to LlamaIndex
Get your token, paste the configuration, and start using 11 tools in under 2 minutes. No API key management needed.