How to Use the Cerebras Inference MCP in LlamaIndex

Index your Cerebras Inference outputs directly into LlamaIndex to build knowledge-heavy RAG applications.

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Connect Cerebras Inference MCP to LlamaIndex

Create your Vinkius account to connect Cerebras 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 Cerebras Inference with LlamaIndex

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Perplexity

Ground LlamaIndex queries in inference data

Feed model responses from `create_completion` into your vector index. You turn raw output into a searchable knowledge base that improves with every run. Stop guessing what your agent said an hour ago. You query your indexed past completions to find specific context for new tasks.

Automate file indexing for Cerebras Inference

Use `upload_file` to push raw data into your inference pipeline. You then index the resulting batch data to keep your retrieval system up to date. Your RAG application stays current without manual data entry. You define the flow, and the server handles the heavy lifting.

Inspect model metadata in LlamaIndex

Retrieve model specs via `get_model` to inform your index configuration. You ensure your RAG system uses the right engine for the specific data type. Check capabilities before you query. You match your index strategy to the specific model features available on the hardware.

Setup guide

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

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Common questions about Cerebras Inference MCP in LlamaIndex

The server tools produce text that you ingest into your index. You then use LlamaIndex to perform semantic search over those results.

It can. You pass user queries to the completion tool to refine them before searching your index.

They are. Once a batch completes, you fetch the output and add it to your local index for future reference.

Vinkius manages the secure connection via a single endpoint token. You don't expose your credentials to the local environment.

Your JSONL batch files reside in a strictly isolated, ephemeral sandbox. The system purges your data as soon as you execute the deletion tool.

Use it with your favorite AI tools

Connect this server to Cursor, Claude, VS Code, and more.

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