Cerebras Inference

Cerebras Inference MCP Connector for Claude

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Access lightning-fast AI inference via Cerebras Wafer-Scale Engine — generate chat completions, manage models, and run batch jobs at record speeds.

15 tools Official Updated Oct 1, 2026 Official Vinkius Partner

Connect to the Cerebras Inference platform to leverage the world's fastest AI inference. This MCP server allows your AI agent to interact with state-of-the-art models like Llama 3.1 and others using the Cerebras Wafer-Scale Engine (WSE) for unprecedented performance.

What you can do

  • Chat & Text Completions — Generate high-speed responses using create_chat_completion and create_completion with support for streaming and tool calling.
  • Model Discovery — Explore available models and their specific details using list_models and get_model to choose the best fit for your task.
  • Batch Processing — Handle large-scale workloads asynchronously with create_batch, list_batches, and cancel_batch for efficient data processing.
  • File Management — Upload and manage JSONL files for batch jobs using upload_file and list_files directly from your agent.
  • Performance Metrics — Monitor your usage and performance metrics to optimize your inference workflows.

How it works

  1. Subscribe to this server
  2. Enter your Cerebras API Key
  3. Start generating tokens at speeds you've never seen before in Claude, Cursor, or any MCP-compatible client.

Who is this for?

  • AI Developers — build and test applications with near-instant model responses to maintain development momentum.
  • Data Scientists — run large-scale batch inference on massive datasets using the asynchronous batch API.
  • Product Teams — integrate high-performance LLMs into production environments where latency is a critical factor.
llm-inferencewafer-scalehigh-speed-aillama3batch-processing

15 tools expose this connector's capabilities to your AI agent.

create_completion

Use this tool only when immediate text generation is required. Generate text continuations from a single prompt string

delete_file

Ensure the file ID is valid before calling. Delete a file

get_batch

Use the unique batch ID to check the job status. Retrieve status of a batch job

get_file

Use the file ID to fetch details. Retrieve metadata for a specific file

get_metrics

Provide a valid organization ID. Retrieve Prometheus-formatted operational metrics

get_model

Provide the model ID as the required parameter. Fetches details for a specific model

list_batches

Do not specify any parameters. List all batch jobs

create_chat_completion

Generate conversational responses using a structured message format

create_batch

Supply the full job configuration payload in the body. Create a batch job for asynchronous processing

get_file_content

Use the file ID to retrieve the content. Download raw content of a file

list_files

Do not specify any parameters. List uploaded files

list_models

Do not specify any parameters. Lists all currently available models

list_public_models

Specify the desired response format. Retrieve model details without an API key

cancel_batch

Provide the batch ID of the job you wish to stop. Cancel a batch job

upload_file

Provide the file upload payload in the body. Upload a JSONL file for Batch processing

See how to talk to your AI agent using Cerebras Inference.

List all available models on Cerebras.

I've retrieved the available models. You can use 'llama3.1-8b', 'llama3.1-70b', or 'gpt-oss-120b' for high-speed inference. Which one would you like to use for your request?

Generate a chat response using llama3.1-8b explaining quantum entanglement.

Using the `create_chat_completion` tool with model 'llama3.1-8b'... Quantum entanglement is a physical phenomenon that occurs when a group of particles are generated, interact, or share spatial proximity in a way such that the quantum state of each particle cannot be described independently...

Check the status of my batch job with ID 'batch_abc123'.

I've checked the status using `get_batch`. The job 'batch_abc123' is currently 'completed'. You can now retrieve the results using the file tools.

Use the `list_models` tool. It will return a list of all supported models, including high-performance options like Llama 3.1, which you can then use in `create_chat_completion`.

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