Pulumi MCP Server for LangChain 11 tools — connect in under 2 minutes
LangChain is the leading Python framework for composable LLM applications. Connect Pulumi through Vinkius and LangChain agents can call every tool natively. combine them with retrievers, memory, and output parsers for sophisticated AI pipelines.
ASK AI ABOUT THIS MCP SERVER
Vinkius supports streamable HTTP and SSE.
import asyncio
from langchain_mcp_adapters.client import MultiServerMCPClient
from langchain_openai import ChatOpenAI
from langgraph.prebuilt import create_react_agent
async def main():
# Your Vinkius token. get it at cloud.vinkius.com
async with MultiServerMCPClient({
"pulumi": {
"transport": "streamable_http",
"url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp",
}
}) as client:
tools = client.get_tools()
agent = create_react_agent(
ChatOpenAI(model="gpt-4o"),
tools,
)
response = await agent.ainvoke({
"messages": [{
"role": "user",
"content": "Using Pulumi, show me what tools are available.",
}]
})
print(response["messages"][-1].content)
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.
LangChain's ecosystem of 500+ components combines seamlessly with Pulumi through native MCP adapters. Connect 11 tools via Vinkius and use ReAct agents, Plan-and-Execute strategies, or custom agent architectures. with LangSmith tracing giving full visibility into every tool call, latency, and token cost.
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 LangChain 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 LangChain via MCP
Follow these steps to integrate the Pulumi MCP Server with LangChain.
Install dependencies
Run pip install langchain langchain-mcp-adapters langgraph langchain-openai
Replace the token
Replace [YOUR_TOKEN_HERE] with your Vinkius token
Run the agent
Save the code and run python agent.py
Explore tools
The agent discovers 11 tools from Pulumi via MCP
Why Use LangChain with the Pulumi MCP Server
LangChain provides unique advantages when paired with Pulumi through the Model Context Protocol.
The largest ecosystem of integrations, chains, and agents. combine Pulumi MCP tools with 500+ LangChain components
Agent architecture supports ReAct, Plan-and-Execute, and custom strategies with full MCP tool access at every step
LangSmith tracing gives you complete visibility into tool calls, latencies, and token usage for production debugging
Memory and conversation persistence let agents maintain context across Pulumi queries for multi-turn workflows
Pulumi + LangChain Use Cases
Practical scenarios where LangChain combined with the Pulumi MCP Server delivers measurable value.
RAG with live data: combine Pulumi tool results with vector store retrievals for answers grounded in both real-time and historical data
Autonomous research agents: LangChain agents query Pulumi, synthesize findings, and generate comprehensive research reports
Multi-tool orchestration: chain Pulumi tools with web scrapers, databases, and calculators in a single agent run
Production monitoring: use LangSmith to trace every Pulumi tool call, measure latency, and optimize your agent's performance
Pulumi MCP Tools for LangChain (11)
These 11 tools become available when you connect Pulumi to LangChain 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 LangChain
Ready-to-use prompts you can give your LangChain 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 LangChain
Common issues when connecting Pulumi to LangChain through the Vinkius, and how to resolve them.
MultiServerMCPClient not found
pip install langchain-mcp-adaptersPulumi + LangChain FAQ
Common questions about integrating Pulumi MCP Server with LangChain.
How does LangChain connect to MCP servers?
langchain-mcp-adapters to create an MCP client. LangChain discovers all tools and wraps them as native LangChain tools compatible with any agent type.Which LangChain agent types work with MCP?
Can I trace MCP tool calls in LangSmith?
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 LangChain
Get your token, paste the configuration, and start using 11 tools in under 2 minutes. No API key management needed.