# Cerebras Inference MCP for AI Agents MCP > Cerebras Inference gives your AI agent access to the Cerebras Wafer-Scale Engine (WSE), delivering industry-leading speed for all large language model tasks. Use this MCP to generate chat responses, run massive batch processing jobs, and discover models at record speeds. It’s built for data scientists and developers who need near-instantaneous LLM performance. ## Overview - **Category:** ai-frontier - **Price:** Free - **Tags:** llm-inference, wafer-scale, high-speed-ai, llama3, batch-processing ## Description Working with huge language models often means waiting forever for a response or struggling to process large datasets sequentially. This MCP changes that entirely. You can connect your agent through Vinkius, giving it access to the Cerebras Wafer-Scale Engine (WSE). What this means in practice is speed at scale. Your agent doesn't just generate chat completions; it does so with a massive boost of processing power. Need to run thousands of prompts against a dataset? You can queue those jobs for asynchronous batch processing, letting your workflow continue while the heavy lifting happens in the background. It’s ideal whether you need quick conversational responses or complex, multi-step data pipelines. When latency is critical—whether for product integration or research—this connection delivers the horsepower needed to keep up with modern AI demands. ## Tools ### cancel_batch Stops a batch job that is currently running or queued. ### upload_file Sends and uploads a JSONL file required for processing in a batch job. ### create_chat_completion Generates responses formatted for structured, back-and-forth conversational dialogue. ### create_completion Outputs continuations of text based on a single input prompt string. ### create_batch Initiates a large-scale, asynchronous job to process many inputs at once. ### delete_file Removes an uploaded file from the system storage. ### get_batch Checks and retrieves the current status and details of a specific batch job. ### get_file_content Downloads the raw text or data content from an uploaded file. ### get_file Retrieves metadata, such as size and owner, for a specific stored file. ### get_metrics Fetches operational usage data in Prometheus format for performance monitoring. ### get_model Retrieves detailed information about a specific model available on the platform. ### list_batches Lists all batch jobs that have been created or are currently pending. ### list_files Shows a list of all files previously uploaded for processing. ### list_models Retrieves a comprehensive list of every model currently supported by the system. ### list_public_models Lists models that do not require an API key to be viewed or selected. ## Prompt Examples **Prompt:** ``` What are the fastest models I can use for chat completions? ``` **Response:** ``` **Available High-Speed Models** | Model Name | Context Window | Max Tokens/sec | | :--- | :--- | :---: | | llama3.1-70b | 8K | 120 | | gpt-oss-120b | 32K | 95 | *Tip*: For general dialogue, `llama3.1-70b` is your best bet for low latency. ``` **Prompt:** ``` I have 5,000 reviews. How do I process them all at once? ``` **Response:** ``` You need to run this as a batch job. **Process Steps:** 1. Upload your JSONL file first (e.g., `upload_file`). 2. Then, initiate the job (`create_batch`). 3. Use `get_batch` periodically to monitor progress and ensure it finishes successfully. Once done, download the results using the file content tool. ``` **Prompt:** ``` List all available models and their capabilities. ``` **Response:** ``` Here are the primary model families: * **Llama 3.1:** Great for conversational flow, high speed. * **GPT-OSS-120b:** Best if you need large context windows for deep analysis. Which one should we check out first? I can run `get_model` to give you the full specs on any of these. ``` ## Capabilities ### Generate Conversational Responses The agent generates structured, high-speed chat completions suitable for dialogue flows. ### Process Large Datasets in Batches You set up large workloads to run asynchronously and retrieve the results when they're ready, perfect for massive data processing. ### Manage Inference Files The agent can upload JSONL files needed for batch jobs and download raw content once the process is complete. ### Discover and Inspect Models You list available models or fetch detailed information to ensure you're using the right engine for your task. ## Use Cases ### Analyzing Customer Feedback at Scale Instead of running a single prompt against 100 customer reviews manually, the agent uses `create_batch` to submit all JSONL files. It processes thousands of records overnight and then retrieves the summarized results using file tools. ### Building Real-Time Chatbots A developer needs a chatbot that feels natural, not robotic. Using `create_chat_completion` ensures the agent handles multi-turn dialogue correctly, making the user experience feel instantaneous. ### Model Comparison for New Features Before committing to a model choice, the Product Lead uses `list_models` and then `get_model` to fetch specific details, ensuring they select the engine that meets both speed and accuracy criteria. ### Cleaning Up Old Jobs A data science project ran a massive batch job by mistake. The engineer quickly uses `list_batches` to find the rogue ID and then calls `cancel_batch` to stop the unnecessary processing immediately. ## Benefits - You get instant conversational responses using `create_chat_completion` and `create_completion`, eliminating chat latency issues. - Manage huge datasets with asynchronous jobs. Use `create_batch` to queue work, and then check status later with `get_batch`. This keeps your agent flow smooth. - Keep track of all your data pipelines by listing all runs using `list_batches` or viewing what files are uploaded via `list_files`. - When you need model details before running a job, use `list_models` to see every supported engine and check which ones match your task requirements. - Monitor performance directly. Call `get_metrics` to gather Prometheus-formatted data on your usage, helping you optimize costs. ## How It Works The bottom line is that you get extremely fast access to advanced LLM processing without worrying about underlying hardware limitations. 1. First, subscribe to this MCP and input your Cerebras API Key into your AI client. 2. Next, instruct your agent on the required action—for example, queuing a batch job or generating a chat completion using a specific model. 3. Finally, the engine executes the task at high speed, returning structured results, status updates, or downloadable files to your agent. ## Frequently Asked Questions **How does Cerebras Inference MCP handle processing huge datasets?** It uses an asynchronous batch API. You upload your data, queue the job, and then check back later for results. This means you don't wait through hours of processing time; your agent just checks when it’s ready. **Is Cerebras Inference MCP better than other LLM APIs for chat?** The strength here is the speed and reliability of the underlying engine. It provides consistently low latency across conversational turns, which makes your application feel much more responsive to the user. **Can I use Cerebras Inference MCP if my model isn't Llama 3?** No problem. The platform supports multiple state-of-the-art models. You can use the listing tools within the MCP to discover and select exactly which engine you need for your specific task. **What if my batch job fails? Can I fix it?** Yes, you can monitor the job status using `get_batch`. If something goes wrong, you can sometimes cancel and restart the process or review the error logs to pinpoint where the failure occurred. **Does Cerebras Inference MCP help with cost optimization?** It helps by allowing efficient resource management. You can use the monitoring tools in the MCP to track your usage and optimize your inference workflows, making sure you're not paying for unused compute time. **How do I get model details using Cerebras Inference MCP?** You simply ask the agent to fetch the model information. The MCP will use `get_model` to retrieve detailed specs, letting you know about context limits and performance before you commit to a job.