How to Use the DeepInfra (Serverless LLM Inference) MCP in LlamaIndex

Q: How does the DeepInfra (Serverless LLM Inference) server improve LlamaIndex RAG?

Your agent calls a tool like createchatcompletion, and LlamaIndex automatically indexes the output. This makes the LLM's live response a searchable part of your knowledge base.

Q: How do I connect my LlamaIndex agent to the DeepInfra (Serverless LLM Inference) server?

Install the llama-index-tools-mcp package and use the McpToolSpec. You point it at your Vinkius server URL, and it turns all the available server tools into a list that you can pass to your agent.

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Connect DeepInfra (Serverless LLM Inference) MCP to LlamaIndex

Create your Vinkius account to connect DeepInfra (Serverless LLM Inference) 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 DeepInfra (Serverless LLM Inference) with LlamaIndex

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Index Live Model Outputs

The `create_chat_completion` tool lets your agent get fresh data from an LLM. LlamaIndex doesn't just use this data once—it indexes it. Now your RAG application can answer questions based on the latest model responses, not just static documents. Use `run_native_inference` for more specialized data, like text extracted from an audio file. Your agent runs the tool, gets the text, and LlamaIndex adds it to a searchable vector store. Your knowledge base now includes data that started as audio.

Query Across Images and Text

Your LlamaIndex agent can use the `generate_image` tool to create a new picture. It can then immediately use `create_chat_completion` to describe that image and index both the image's metadata and the description. This creates a multi-modal knowledge base. You can later ask your agent, 'find images of modern architecture' and it can find them because it indexed the descriptions it generated itself. This MCP server provides the raw materials.

A LlamaIndex-Ready MCP Server

LlamaIndex is built on embeddings. The `create_embedding` tool gives your agent direct access to DeepInfra's models for this exact purpose. Instead of relying on a default model, you can point your agent at this MCP tool. It will send text to DeepInfra and get back vectors ready to be loaded into your index. It's a simple way to control the embedding process within your LlamaIndex application.

Setup guide

Set up DeepInfra (Serverless LLM Inference) 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 DeepInfra (Serverless LLM Inference) 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 DeepInfra (Serverless LLM Inference) tools.",
)
response = await agent.run("List recent DeepInfra (Serverless LLM Inference) data")

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Common questions about DeepInfra (Serverless LLM Inference) MCP in LlamaIndex

Your agent calls a tool like `create_chat_completion`, and LlamaIndex automatically indexes the output. This makes the LLM's live response a searchable part of your knowledge base.

Yes, that's the core idea of using them together. LlamaIndex indexes the tool outputs, so you can run semantic queries against them later. It turns API calls into a persistent, searchable asset.

Install the `llama-index-tools-mcp` package and use the `McpToolSpec`. You point it at your Vinkius server URL, and it turns all the available server tools into a list that you can pass to your agent.

You could, but this MCP server saves you the work. The adapter handles the API schema, authentication, and error handling for all the tools, so you can focus on building your RAG application.

Your prompts, any text for embedding, and selected model names are passed through Vinkius to DeepInfra. This data is processed ephemerally within isolated sandboxes and is not logged or stored by Vinkius.

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