Groq MCP. Ultra-fast LLM inference and media processing.

Q: Can I use the Groq MCP Server for both transcription and translation?

Yes. Use transcribeaudio to get plain text, or use translateaudio to get a synchronized English text version of non-English audio.

Q: Is the structuredoutput tool reliable?

Yes, the structuredoutput tool constrains the AI's generation to a strict JSON format. This eliminates the risk of the model adding explanatory text or stray characters.

Q: Does the Groq MCP Server handle model discovery?

Yes, the getmodel and listmodels tools let your agent check available models and retrieve their specific metadata before making a call.

Q: How do I manage model availability using the listmodels tool?

The listmodels tool shows all available models. You can use this to check model IDs and versions before calling other tools, ensuring your agent targets a high-speed, active instance.

Claude

ChatGPT

Cursor

Gemini

Windsurf

VS Code

JetBrains

Vercel

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Works with every AI agent you already use

…and any MCP-compatible client

Just plug in your AI agents and start using Vinkius.

Groq MCP Server. Get blazing-fast LLM inference by connecting your AI agent to Groq's LPU-accelerated endpoints. Run chat completions using Llama 3 or Mixtral, transcribe audio files, translate non-English audio to English text, and enforce structured JSON output—all with minimal latency.

What your AI agents can do

Chat completion

Generates a chat completion using Llama, Mixtral, or Gemma models at ultra-fast inference speeds.

Create embedding

Creates numerical embeddings from text input for vector storage and retrieval.

Get model

Retrieves specific details and metadata about an available Groq model.

+ 5 more capabilities included

Generate Chat Completions

Runs text generation using Llama, Mixtral, or Gemma models at ultra-fast speeds.

Create Text Embeddings

Generates numerical vectors for text chunks to power semantic search and RAG systems.

Retrieve Model Metadata

Pulls details about specific Groq models, like context window size or supported features.

List Available Models

Returns a list of all high-speed models currently available on the Groq platform.

Check Content Safety

Runs text or content through a moderation check to flag unsafe or prohibited material.

Enforce JSON Output

Forces the AI to generate text that strictly adheres to a valid JSON schema, perfect for database writing.

Transcribe Audio Files

Converts an audio file into a plain text transcript using optimized Whisper models.

Translate Audio Files

Takes non-English audio and outputs a synchronized, readable English text translation.

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Supported MCP Clients

Claude

ChatGPT

Cursor

Gemini

Windsurf

VS Code

JetBrains

Vercel

+ other MCP clients

Free for Subscribers

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AI Agent

Groq MCP Server: 8 Tools for AI Inference & Media

These tools let your AI agent generate text, process audio, or structure data using Groq's high-speed, LPU-accelerated endpoints.

chat019d75ab

chat completion

Generates a chat completion using Llama, Mixtral, or Gemma models at ultra-fast inference speeds.

create019d75ab

create embedding

Creates numerical embeddings from text input for vector storage and retrieval.

get019d75ab

get model

Retrieves specific details and metadata about an available Groq model.

list019d75ab

list models

Lists all model IDs and versions currently available for inference.

moderate019d75ab

moderate content

Checks a given piece of content for safety violations or policy breaches.

structured019d75ab

structured output

Forces the AI to output data that strictly matches a defined JSON format.

transcribe019d75ab

transcribe audio

Converts audio files into a readable text transcript.

translate019d75ab

translate audio

Converts non-English audio files into written English text.

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Turn any API into an MCP. Import a spec, define Agent Skills, or deploy with MCPFusion.

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What you can do with this MCP connector

Groq MCP Server - Ultra-fast LLM Inference

Connect your AI agent to Groq's LPU-accelerated endpoints. You get blazing-fast LLM inference and full control over your generative AI workflows. Use chat_completion to run text generation with Llama, Mixtral, or Gemma models at ultra-fast speeds. You can create numerical embeddings from text input using create_embedding for vector storage and retrieval.

Need to know what models are available? You'll use list_models to see all model IDs and versions, and get_model to pull specific details about any Groq model. You can check content safety using moderate_content to flag unsafe or prohibited material. If you need the AI to output data that strictly matches a defined JSON format, use structured_output.

You can convert audio files to plain text transcripts with transcribe_audio, and you'll use translate_audio to take non-English audio and output a readable English text translation.

How Groq MCP Works

1 Subscribe to the Groq server and provide your Groq API Key in the client settings.
2 Your AI client sends a request to the server (e.g., 'Transcribe this file').
3 The server executes the necessary tool call on Groq's LPU architecture and returns the result.

The bottom line is, you get sub-second, hardware-accelerated AI results directly in your chat or IDE.

Who Is Groq MCP For?

AI developers who need proof-of-concept speed, or data scientists building production pipelines that handle multimodal inputs. If your app needs to process audio and text fast, you're here. It's for anyone whose workflow gets bottlenecked by slow API calls.

AI Developer

Tests and debugs complex LLM prompts and tool-calling logic with minimal latency, ensuring their agents work correctly.

Software Engineer

Generates structured JSON data from natural language inputs or transcribes audio files directly from their IDE or terminal.

Data Scientist

Evaluates different open-source model performances on Groq's LPU architecture, comparing throughput and latency.

What Changes When You Connect

Speed: Chat completions using Llama 3 or Mixtral run with LPU acceleration, meaning your agent gets responses in fractions of a second. This is critical for good user experience.
Multimodal Workflow: Handle complex inputs easily. You can transcribe audio with transcribe_audio and immediately pass that text to chat_completion for summarization.
Data Reliability: Never trust raw LLM output. Use structured_output to guarantee the AI returns perfect, valid JSON, making it ready for database writes.
Global Reach: Process audio from any language. Run translate_audio to get immediate, synchronized English text, eliminating the need for external translation APIs.
System Control: Monitor your setup with get_model and list_models. You always know exactly which model and version your agent is using.
Safety & Compliance: Use moderate_content to filter all input and output data, keeping your application secure and compliant by design.

Real-World Use Cases

Building a Customer Support Bot

A support agent needs to handle incoming audio calls. They ask their agent to run transcribe_audio on the recording. The agent feeds the resulting text into chat_completion to summarize the issue and then uses structured_output to log the ticket details into a structured format. The problem is solved in one conversational flow.

Analyzing Foreign Market Interviews

A market researcher records interviews in Mandarin. Instead of manually transcribing and translating, they ask their agent to run translate_audio on the file. They get immediate, readable English text, which they can then feed into create_embedding to build a knowledge base.

Streaming Real-Time Code Assistance

A software engineer is coding and needs fast context. They use their agent to run chat_completion with Llama 3 on a large code block, getting near-instant responses. This lets them debug or write code without the typical API lag.

Automating Form Submission from Chat

A product manager wants their agent to capture user requirements. They prompt the agent to run structured_output and specify a JSON schema for 'Feature Request'. The agent outputs the data, and the PM can pipe that JSON directly into a ticketing system.

The Tradeoffs

Sequential API Calls

Calling transcribe_audio and then calling translate_audio in separate script steps. The script waits for the first file to finish, then starts the second, creating long, synchronous delays.

→ Chain the tools together. Use the output of transcribe_audio (or translate_audio) as the input for the next step. This keeps the workflow flowing and minimizes idle time.

Ignoring Model Versions

Using a generic 'chat_completion' call without checking if the model supports the necessary context size or tool-calling logic. This results in runtime errors or truncated output.

→ First, run list_models to see what's available, then use get_model to confirm the exact model ID and capabilities before running chat_completion.

Relying on Free Text Output

Asking the LLM to summarize data and then trying to parse the resulting block of text into a database record. This fails when the LLM changes its formatting or adds explanatory text.

→ Always force structured output. Use structured_output with a rigid JSON schema. The AI output is guaranteed to be machine-readable data, period.

When It Fits, When It Doesn't

Use this server if your application needs to process audio, text, and structured data rapidly, and you need reliable, low-latency inference. You must use it if your core workflow involves: 1) Transcribing/translating audio; 2) Generating data that needs to be consumed by another system (JSON); or 3) Needing the fastest possible chat responses (LPU acceleration).

Don't use it if your goal is simple, single-turn question answering that doesn't involve media or structure. If you only need to call a single, simple external API endpoint, you might be better off using a specialized, single-purpose connector. But if you're building a complex, multi-step agent, this is the one.

Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Groq. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.

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Works with Claude, ChatGPT, Cursor, and more

The Model Context Protocol standardizes how applications expose capabilities to LLMs. Instead of operating in isolation, your AI gains direct access to external platforms, live data, and real-world actions through secure, standardized connections.

This server provides 8 capabilities that interface natively with Claude, ChatGPT, Cursor, and any MCP client. No middleware. No custom integration required.

Available Capabilities

chat_completion create_embedding get_model list_models moderate_content structured_output transcribe_audio translate_audio

Waiting on API responses kills flow.

Today, building an agent that handles multimodal input means a lot of copy-pasting and waiting. You transcribe a meeting recording in one service, download the file, upload it to a second service, wait for the text, and then manually feed that text into a third service to get a summary. It's slow, and the process breaks if any step fails.

With the Groq MCP Server, you skip the manual steps. Your agent runs `transcribe_audio` directly, gets the text, and immediately passes it to `chat_completion` for summarization—all within the same conversation flow. The result is immediate, reliable, and contained.

Structured Output with Groq MCP Server

If you ask an LLM to generate a list of meeting action items, the output is usually a messy paragraph: 'John needs to call marketing. Sarah should review the budget by Friday.' You then have to write code to parse out names, actions, and deadlines.

Now, you simply enforce structure. Using the `structured_output` tool, you tell the model exactly what JSON format you expect. The output is guaranteed, so you can pipe it straight into a database or a Jira ticket without a single line of parsing code.

Common Questions About Groq MCP

How does the Groq MCP Server improve my LLM speed? +

It utilizes Groq's LPU-accelerated endpoints, which deliver chat completions at extremely low latency. This means your agent feels instant, making the overall application feel much snappier.

Can I use the Groq MCP Server for both transcription and translation? +

Yes. Use transcribe_audio to get plain text, or use translate_audio to get a synchronized English text version of non-English audio.

Is the structured_output tool reliable? +

Yes, the structured_output tool constrains the AI's generation to a strict JSON format. This eliminates the risk of the model adding explanatory text or stray characters.

What models can I use with the chat_completion tool? +

You can use Llama 3, Mixtral, and Gemma models for chat completions. You can check model availability using list_models.

Does the Groq MCP Server handle model discovery? +

Yes, the get_model and list_models tools let your agent check available models and retrieve their specific metadata before making a call.

How do I manage model availability using the list_models tool? +

The list_models tool shows all available models. You can use this to check model IDs and versions before calling other tools, ensuring your agent targets a high-speed, active instance.

What is the purpose of the structured_output tool? +

It forces the AI to generate output in rigid JSON format. This is critical for automating data entry and integrating the results into downstream systems reliably.

Can the chat_completion tool handle complex tool-calling logic? +

Yes, the chat completion tool supports tool calling. You can bind external definitions and let your agent interact with specialized tools using a secure JSON architecture.

How fast are Groq's chat completions compared to standard GPUs? +

Groq's LPU architecture is designed for extreme low-latency inference, often delivering hundreds of tokens per second. Your agent uses the 'chat' tool to execute these blazing-fast requests, returning AI responses almost instantly.

Can my agent transcribe long audio files using Groq Whisper? +

Yes. Use the 'transcribe' tool. Provide the public URL of your audio file and select a Whisper model (e.g., 'whisper-large-v3'). The agent will parse the stream and return the full text transcript flawlessly.

How do I ensure the AI response is formatted as valid JSON via chat? +

Use the 'chat_json' tool. This activates Groq's JSON mode, which explicitly constrains the text inference to rigid, valid JSON formatting, making it perfect for direct system integrations.

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