How to Use the Lambda Labs (GPU Cloud) MCP in LlamaIndex

Q: Can LlamaIndex tell me which GPUs are available on Lambda Labs (GPU Cloud)?

Yes. You set up a query engine that calls listinstancetypes. When you ask 'are there any H100s free?', LlamaIndex runs the tool and uses the live output to answer.

Q: How does LlamaIndex help manage multiple instances on Lambda Labs (GPU Cloud)?

It indexes the output of listinstances. You can then ask questions like 'what are the IPs of all running instances?' and get a consolidated answer based on the indexed data.

Q: Can I use LlamaIndex to find the cost of a specific GPU on Lambda Labs (GPU Cloud)?

Absolutely. The listinstancetypes tool includes pricing data. You build a RAG agent that can query this indexed information to answer questions about cost per hour for any GPU type.

Turn your Lambda Labs GPU fleet into a queryable knowledge base with LlamaIndex. Ask questions about your instances, configs, and costs.

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Connect Lambda Labs (GPU Cloud) MCP to LlamaIndex

Create your Vinkius account to connect Lambda Labs (GPU Cloud) to LlamaIndex 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 Lambda Labs (GPU Cloud) with LlamaIndex

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Create a Searchable Index of Your GPUs

Use LlamaIndex to build a RAG pipeline that indexes your live infrastructure. Run `list_instances` and `list_instance_types` and feed the output directly into a vector store. Now you can ask natural language questions like, 'how many A100s are we running right now?' This creates a living document of your cloud environment. When you `launch_instance` or `terminate_instances`, the changes can be re-indexed. Your knowledge base is never stale because it's built from the direct output of the tools in this MCP server.

Ground Answers in Real-Time Data

Stop guessing about your setup. LlamaIndex can use `get_instance` to fetch the specific configuration of any running machine, including its IP and SSH details. This data grounds your agent's responses in reality, not a hallucination. You can also index your storage configurations. By calling `list_filesystems` and `list_ssh_keys`, your agent can answer questions like 'which filesystem is attached to instance X?'. The answers are based on facts straight from the API.

Build a DevOps RAG App with this MCP Server

Combine these tools to build a powerful internal application. A DevOps engineer can ask 'What are the specs and pricing for available H100 instances?' and LlamaIndex will call `list_instance_types` to get the data and synthesize a direct answer. This isn't just a chatbot. It's a query engine for your infrastructure. By connecting this MCP server to LlamaIndex, you build a system that can both act on your cloud (`launch_instance`) and reason about its current state.

Setup guide

Set up Lambda Labs (GPU Cloud) MCP in LlamaIndex

Prerequisites

Python 3.10+ installed
llama-index-tools-mcp package
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai. The MCP tools package provides BasicMCPClient and McpToolSpec.
2

Connect with BasicMCPClient
Point BasicMCPClient to your Vinkius endpoint URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. Supports SSE and Streamable HTTP transports.
3

Convert to LlamaIndex tools
Call mcp_tool_spec.to_tool_list_async() to convert all Lambda Labs (GPU Cloud) MCP tools into native FunctionTool objects that any LlamaIndex agent can use.
4

Run with any LLM
Create a FunctionAgent with the tools and your preferred LLM. Swap OpenAI for Anthropic, Gemini, or any LlamaIndex-supported provider.

agent.py

from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI

# Connect to the MCP
mcp_client = BasicMCPClient(
    "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)
mcp_tool_spec = McpToolSpec(client=mcp_client)

# Convert MCP tools to LlamaIndex tools
tools = await mcp_tool_spec.to_tool_list_async()

# Create and run the agent
agent = FunctionAgent(
    tools=tools,
    llm=OpenAI(model="gpt-4o"),
    system_prompt="You have access to Lambda Labs (GPU Cloud) tools.",
)
response = await agent.run("List recent Lambda Labs (GPU Cloud) data")

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Common questions about Lambda Labs (GPU Cloud) MCP in LlamaIndex

Yes. You set up a query engine that calls `list_instance_types`. When you ask 'are there any H100s free?', LlamaIndex runs the tool and uses the live output to answer.

It indexes the output of `list_instances`. You can then ask questions like 'what are the IPs of all running instances?' and get a consolidated answer based on the indexed data.

Absolutely. The `list_instance_types` tool includes pricing data. You build a RAG agent that can query this indexed information to answer questions about cost per hour for any GPU type.

LlamaIndex lets you query your infrastructure state using natural language. It turns raw JSON from this MCP server's tools like `get_instance` into a searchable knowledge base, so you can ask complex questions without writing code.

The tools only touch infrastructure metadata: instance types, IP addresses, filesystem names, and SSH key identifiers. All data is passed through an ephemeral, sandboxed environment on Vinkius. Your private data on the instances is never accessed.

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