Groq MCP. Ultra-Fast Inference for Media and Logic.
Groq MCP delivers ultra-fast LLM inference by leveraging LPU hardware acceleration directly through your AI client. It lets you run chat completions on models like Llama 3 and Mixtral with blazing speed, while also handling complex media tasks. You can transcribe audio streams into text, translate non-English speech immediately to English, or force the output into rigid JSON formats for system integration.
Claude 
ChatGPT 
Cursor 
Gemini 
Windsurf 
VS Code 
JetBrains 
Vercel Give Claude and any AI agent real-world access
Run text generation, using chat_completion, against accelerated hardware endpoints supporting Llama and Mixtral.
Transcribe audio files into accurate language transcripts using the transcribe_audio tool.
Take non-English audio and retrieve immediate text translations exclusively in English via translate_audio.
Constrain AI inference to output only valid JSON format using structured_output, perfect for automating data pipelines.
Create high-quality text embeddings using create_embedding for advanced retrieval and context building.
Check available models or retrieve detailed metadata about specific LLMs through list_models and get_model.
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What AI agents can do with Groq: 8 Powerful Tools for Accelerated Inference
These tools let you perform every step of a complex AI workflow. You can chat, transcribe media, generate embeddings, or force structured JSON output with simple commands.
Make your AI actually useful.
Add this MCP to Claude, Cursor, or Windsurf and your AI stops guessing. It gets real tools to look things up, take action, and handle the stuff you keep doing by hand.
Start using Groq MCPChat Completion
Generates a response using Llama, Mixtral, or Gemma models with ultra-fast inference speed.
List Models
Retrieves a list of all available high-speed language models you can use.
Get Model
Fetches specific metadata and details about any particular model.
Create Embedding
Converts text into vector embeddings, which allows your AI agent to understand...
Transcribe Audio
Takes an audio file and converts the spoken word into a written transcript.
Translate Audio
Converts non-English audio files into English text translations.
Moderate Content
Checks any given content to determine if it violates safety guidelines.
Structured Output
Forces the AI model to generate output that strictly adheres to a predefined JSON...
Security and governance baked right in.
Pick your AI client below to get set up. Just create a Vinkius account, subscribe, and you're instantly up and running. We handle the entire backend infrastructure, delivering out-of-the-box support for HTTPS Streamable, SSE, and OAuth2—zero messy routing required.
Claude AI
Open Claude Settings
Go to claude.ai, click your profile icon, then navigate to Customize → Connectors.
Add Custom Connector
Click the "+" button and select Add custom connector. Paste your Vinkius endpoint URL:
https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp
Replace [YOUR_TOKEN_HERE] with your token
from cloud.vinkius.com. For OAuth-protected servers, expand
Advanced settings to add credentials.
Start a conversation
Open a new chat. The Groq integration is available immediately — no restart needed.
Choose How to Get Started
Build a custom MCP for your own tools, or connect a ready-made integration from our catalog.
Build Your Own
Turn any API into an MCP. Import a spec, define Agent Skills, or deploy with MCPFusion.
- Import from OpenAPI, Swagger, or YAML specs
- Create Agent Skills with progressive disclosure
- Deploy to edge with MCPFusion framework
- Built in DLP, auth, and compliance on each call
- Real time usage dashboard and cost metering
- Publish to catalog or keep private
Make Your AI Do More
Start with Groq, then connect any of our 5,200+ other servers whenever your AI needs more. One click, no limits.
- Use this MCP plus 5,200+ others, all in one place
- Add new capabilities to your AI anytime you want
- Connections are secured and governed automatically
- Track usage and costs across all your servers
- Works with Claude, ChatGPT, Cursor, and more
- New servers added to the catalog weekly
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.
VINKIUS CLOUD
Cloud Hosted
Managed infra
V8 Isolated
Sandboxed per request
Zero-Trust Proxy
No stored credentials
DLP Enforced
Policy on each call
GDPR Compliant
EU data residency
Token Compression
~60% cost reduction
Dealing with messy data streams today is brutal.
You record an international meeting. You export the raw MP3. Then, you have to upload it to one tool for transcription, download a massive text file, and finally copy-paste that whole thing into another service just to get an English summary. It’s a painful loop of uploads, downloads, and manual copy/pasting across three different tabs.
With this MCP, the process collapses. Your agent takes the audio file once. It handles transcription with transcribe_audio and then immediately translates the text using translate_audio, giving you a clean English transcript in minutes, not hours.
Groq gives your agents perfect data structure.
Before this, when an LLM gave you information—say, about a product—you'd get paragraphs of text. You'd have to manually search for the price, the name, and the category, then copy those three pieces into your internal form.
Now, using structured_output, you ask for the data once. The agent responds with flawless JSON that is ready to be piped directly into your system. No parsing required.
What Groq MCP does for your AI
Connect this MCP to your preferred AI client to gain full control over high-speed generative AI and multimodal workflows. Instead of waiting minutes for complex requests, you run everything—from simple text generation to audio processing—at hardware speed using Groq's LPU architecture. You can instruct the agent to transcribe an audio file, then immediately translate that resulting text into English.
Need data for a database? Use structured output to force the AI response into perfect JSON format, eliminating messy parsing steps later on. Furthermore, you don't have to worry about model compatibility; you can use tools like list_models and get_model to check exactly what high-speed models are available before running your main chat completions or creating embeddings for context.
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How to set up Groq MCP
The bottom line is that instead of managing separate APIs for speed, media, or structure, everything runs through one unified, blazing-fast connection point.
First, subscribe to this MCP and enter your Groq API Key. You'll find the key in your Groq Cloud Dashboard under API Keys.
Next, connect it to your AI client—like Cursor or Claude—through Vinkius. Your agent now sees all available high-speed tools.
Finally, you prompt your agent with a complex request, and it executes the necessary actions (e.g., transcribe_audio, followed by translate_audio) using accelerated hardware.
Who uses Groq MCP
This MCP is built for developers and data scientists who hit a wall with standard API latency. If your workflow requires combining fast language generation with media processing or strict data typing, this is what you need. It’s for anyone whose job involves moving raw, messy data into clean, actionable formats instantly.
You're debugging complex LLM prompts and tool-calling logic; Groq helps you test these flows with sub-second latency.
You need to take an audio recording, transcribe it, and then use the text results to populate a database schema in JSON format directly from your IDE.
You're comparing different open-source model performances on specialized hardware without having to manage multiple cloud endpoints.
Benefits of connecting Groq MCP
You get immediate results when generating text. Using chat_completion means you're not stuck waiting on slow endpoints; responses arrive almost instantly, letting you build real-time applications.
Your data pipelines become reliable. Instead of hoping the AI gives readable output, using structured_output forces it into perfect JSON, making post-processing trivial and bug-free.
You handle global content without friction. If you need to process audio from a non-English speaker, combine transcribing with translate_audio to get immediate English text.
Context retrieval is fast and accurate. By running create_embedding first, your agent can pull relevant knowledge from massive datasets quickly, ensuring the LLM responds with highly specific information.
Model management happens in context. You don't guess which model works best; you use list_models to check availability before initiating a complex workflow.
Groq MCP use cases
Analyzing international meeting transcripts
An operations team member records an audio meeting in Mandarin. They ask their agent to first transcribe the entire file using transcribe_audio, and then immediately run translate_audio on that transcript to get actionable English notes.
Building a structured knowledge base
A data scientist uploads 10 research papers. They use create_embedding to index the content. Later, they ask their agent a question and retrieve the answer using chat_completion, grounded by the indexed context.
Automating form submission data
A developer needs an agent to process user input text about a new product. They use structured_output to force the AI to return a clean JSON object containing specific fields like 'product name,' 'price,' and 'category' for immediate API insertion.
Testing model capabilities pre-launch
A product team needs to know if their new agent can handle different models. They use get_model to check the metadata and context window size of Mixtral before running a final, high-stakes chat completion test.
Groq MCP tradeoffs
What to watch out for, and the recommended way to handle each one.
Handling structured data manually
Asking an agent general questions and then spending 20 minutes writing Python code to parse the resulting text block into keys and values.
Just use structured_output. Tell your agent exactly what JSON format you need, and it gives you clean, ready-to-use data every single time.
Ignoring audio source languages
Trying to run a simple transcription tool on Spanish audio and getting gibberish because the model wasn't designed for translation.
Use the specialized translate_audio tool. It handles both the initial transcription and the immediate cross-lingual conversion into English.
Relying on general purpose APIs
Using a standard, non-accelerated API for chat completions, resulting in noticeable delays that break the user's flow.
Connect this MCP. The LPU acceleration through chat_completion slashes latency down so fast it feels instantaneous.
When to use Groq MCP
Use this MCP if your workflow involves combining multiple data types or demanding perfect structure. If you need to go from spoken word (audio) to text, and then translate that text into a specific language, you need the audio processing tools here. Similarly, if any piece of LLM output needs to feed directly into a database or another service, structured_output is non-negotiable because it guarantees clean JSON. Don't use this MCP if your only task is writing a simple email draft; then a basic text completion tool will do fine. But if that 'simple email' requires you to first summarize an attached audio file and embed the result into a knowledge base, you need everything Groq offers.
Frequently asked questions about Groq MCP
Does Groq MCP support multiple file types? +
Yes, this MCP handles both text and audio files. You can use transcribe_audio on an MP3 or WAV file and then process the resulting text.
How do I make sure the output is usable in my database using Groq? +
Use structured_output with the tool. By defining a rigid JSON schema, you guarantee that the AI response will match the exact format your database expects.
Can Groq MCP handle audio translation and transcription together? +
Absolutely. You can chain these operations. First, transcribe_audio captures the speech, and then translate_audio takes that output to provide a clean English text file.
Why should I use Groq MCP for embeddings instead of another service? +
Groq provides extremely fast context generation. Using create_embedding ensures your knowledge base is updated and searchable with minimal latency, keeping your agents responsive.
What models can chat_completion access on Groq MCP? +
The chat_completion tool supports several high-performance open-source models, including Llama 3, Mixtral, and Gemma, all optimized for speed.
Powerful workflows you can unlock today
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