Together AI MCP. Power Multi-Modal AI with Open Source Models
Together AI connects your AI agent to over 100 open-source models, giving you a unified platform for everything from text chat and image creation to audio transcription and model fine-tuning. It powers advanced generative AI applications without requiring you to manage any cloud infrastructure.
Claude 
ChatGPT 
Cursor 
Gemini 
Windsurf 
VS Code 
JetBrains 
Vercel Give Claude and any AI agent real-world access
Your agent can generate high-quality text responses for conversations using various open-source models.
The MCP handles generating realistic images or full videos based on simple text prompts.
You can convert spoken words into written transcripts, or turn plain text into natural-sounding speech for voiceovers.
It generates vector embeddings from documents and reranks results so your agent finds the most relevant information quickly.
You can run fine-tuning jobs, upload data files, and manage dedicated endpoints for reliable performance.
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What AI agents can do with Together AI: A Powerful Toolset With 27 Tools
These tools let you manage model lifecycle, generate media, process voice and text data, and run large background jobs all through one connection.
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 Together AI MCPCreate Audio Speech
This tool generates speech from plain text, creating voiceovers for your content.
Create Audio Transcription
It converts an uploaded audio file into a written transcript using speech-to-text...
Cancel Batch
You can stop any large, running background processing job immediately.
Create Chat Completion
This tool generates model responses by simulating a full back-and-forth chat...
Create Batch
It starts a new, large-scale asynchronous job that runs in the background over time.
Create Endpoint
You can set up a dedicated connection point to ensure your model performance never drops or slows down.
Create Fine Tune
This initiates the process of training an open-source model on your specific, proprietary dataset.
Delete Endpoint
It removes a dedicated connection point you previously set up for performance...
Delete File
This permanently deletes an uploaded file used for training or batch processing.
Delete Fine Tune
You can cancel a fine-tuning job that you started and no longer need.
Create Embeddings
It takes any block of text and converts it into numerical vector embeddings for...
Get Batch
You can check the current status and results of a specific background job.
Get Endpoint
This retrieves all the details about a dedicated model endpoint you created.
Get File
It fetches metadata and information about an uploaded file without needing to...
Get Fine Tune
You get the current status and progress report for a specific fine-tuning job.
Create Image Generation
This tool generates brand new images based on detailed text descriptions or prompts.
List Batches
You see a list of all background jobs that have been created using the system.
List Endpoints
It lists every dedicated model endpoint currently running or configured for your account.
List Files
You get a list of all data files you've uploaded to the system.
List Fine Tune Checkpoints
This lists saved versions, or checkpoints, for a fine-tuning job so you can revert...
List Fine Tunes
It gives you an overview of all the fine-tuning jobs that have been run previously.
List Models
You can see a list of every model available for use through this MCP connection.
Create Rerank
This tool reorders documents based on how relevant they are to the user's specific...
Create Text Completion
It generates extended text content for a simple prompt, ideal for articles or summaries.
Update Endpoint
You can change the status—like scaling up or down—of an existing dedicated model endpoint.
Upload File
It securely uploads a file for use in fine-tuning, evaluation, or batch processing...
Create Video Generation
This tool creates entire videos from text prompts or by animating an existing image.
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 Together AI 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 Together AI, 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 Together AI. 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
The headache of piecing together AI features manually.
Today, building a single feature that needs to do three things—like reading an audio file, summarizing it, and then generating promotional art—is a nightmare. You're jumping between the transcription tool, the chat API, and the image generation platform. You copy text from one dashboard into another service, manage keys for multiple providers, and spend hours just stitching the workflow together.
With this MCP, your agent handles the whole sequence inside one connection point. It takes the audio input, runs `create_audio_transcription`, passes that output to generate a summary via chat completion, and finally feeds keywords into `create_image_generation`. You get a fully functional feature without ever leaving your client.
Generating Media with Dedicated Model Operations
The biggest manual step that disappears is the juggling act between different model APIs. You used to have separate documentation and setup steps just for generating an image versus generating a video, forcing you into complex multi-step code blocks.
Now, if your workflow needs visual content, whether it's basic text prompts or full motion video, you call `create_image_generation` or `create_video_generation`. The whole process is contained and controllable from one place.
What Together AI MCP does for your AI
You can connect this MCP to your agent to access the world's fastest inference cloud for open-source models. This connector gives you a complete toolkit for generative AI, handling everything from basic text chat and creating stunning images to processing audio files or training custom model checkpoints. Need to build complex search features? You generate vector embeddings and rerank documents using specialized tools.
Plus, if your application needs constant performance, you can create dedicated endpoints with predictable scaling. Whether you're building an app that talks, draws pictures, or analyzes voice recordings, this MCP keeps all the power running through a single connection point via Vinkius.
019e38fc-f902-730c-94c9-64868c3fd057 
How to set up Together AI MCP
The bottom line is that you use your AI client to trigger advanced generative tasks without having to worry about managing underlying model servers.
First, subscribe to this MCP and provide your Together AI API Key.
Next, your agent uses the connection to call specific tools—for example, telling it to create a video or generate embeddings.
Finally, you get back the resulting data payload, like an image URL or a transcript file path.
Who uses Together AI MCP
This MCP serves developers who build consumer-facing AI tools, data scientists needing custom models, and product managers trying to rapidly prototype multi-modal features. If your job involves integrating more than one type of AI output (text, image, audio), this is for you.
They use the MCP to connect text generation alongside media tools, building a single conversational agent that can both talk and draw.
They leverage the model management capabilities, uploading custom datasets for fine-tuning and creating vector embeddings for complex retrieval tasks.
They use this to prototype features that require multiple inputs and outputs—like turning user text into a video mockup or an audio ad copy.
Benefits of connecting Together AI MCP
You don't worry about infrastructure. By connecting this MCP, you get immediate access to over 100 open-source models for text, image, and audio tasks.
When performance matters, you create dedicated endpoints using create_endpoint, ensuring your app never slows down due to model throttling.
Need custom intelligence? You upload data and use create_fine_tune to train a specialized model on your unique business vocabulary.
Build advanced search. Instead of simple keyword matching, you generate embeddings with create_embeddings and then refine results using create_rerank for better accuracy.
Handle large-scale workloads easily. Use the batch tools (create_batch, list_batches) to process thousands of items asynchronously without timing out your agent.
Together AI MCP use cases
Building a Podcast Summary Generator
A user uploads an hour-long interview audio file. The agent first runs create_audio_transcription to get the text transcript, then uses create_chat_completion on that text to draft five key bullet points, and finally sends those points via a messaging tool.
Creating Marketing Assets for a Product Launch
The product team inputs a core feature description. The agent uses create_image_generation to generate several visual concepts and then runs create_video_generation on the best image, all within one workflow.
Implementing Advanced Internal Knowledge Search
Instead of just searching a database, the agent takes user questions, uses create_embeddings to convert them and the documents into vectors, and then runs create_rerank to pull back the absolute most relevant internal policy document.
Automating Customer Service Voice Guides
The system takes a support article written by an expert. It uses create_audio_speech to convert that text into a professional voice guide, ready for immediate deployment.
Together AI MCP tradeoffs
What to watch out for, and the recommended way to handle each one.
Using generic LLM APIs
The developer just calls the basic chat completion tool and assumes it has enough context or specialized knowledge for complex tasks like audio analysis.
For specific, multi-modal needs, don't rely on a single endpoint. You must use create_audio_transcription first to extract text, then feed that structured data into create_chat_completion.
Running tasks synchronously
The developer tries to process 500 documents in a single API call because it's faster to code.
For anything over a few dozen items, you have to use the batch system. Start by calling create_batch and then monitor progress using get_batch.
Ignoring performance needs
The application fails or slows down during peak usage hours because it's relying on shared, general-purpose resources.
Set up stability first. Use create_endpoint to secure a dedicated resource for your critical model calls, guaranteeing predictable speed.
When to use Together AI MCP
Use this MCP if your application needs to handle multiple types of AI output—for instance, generating an image and writing the accompanying alt text, or transcribing audio and summarizing it. If you're building a system that requires specialized data handling like embedding generation or fine-tuning on private documents, you need its advanced model operations. Don't use this if your only goal is simple API calls to a single general chat model; in those cases, a simpler text completion tool might suffice. But if the complexity involves media (audio/video), structured knowledge retrieval (create_embeddings), or reliable scaling (create_endpoint), then you need the power of this entire catalog.
Frequently asked questions about Together AI MCP
How do I use the Together AI MCP for document search? +
You run this by first calling create_embeddings on your documents to turn them into vectors. Then, when a user asks a question, you use create_rerank to find the most relevant chunks of text from those stored embeddings.
Can I make my AI model better using this MCP? +
Yes. You manage custom training jobs by calling upload_file and then initiating a job with create_fine_tune. This allows you to teach the open-source models your company's specific jargon.
What is the difference between `create_chat_completion` and `create_text_completion`? +
Use create_chat_completion when you need the model to remember context from a conversation history. Use create_text_completion for single, self-contained text generation tasks like writing an article summary.
Does this MCP help with large data uploads? +
It handles massive jobs using the batch tools. You start a job via create_batch, and then you monitor its progress and retrieve results later using get_batch.
How do I ensure my model stays fast for production? +
You use create_endpoint. This tool establishes a dedicated, stable connection point that isolates your usage from general traffic fluctuations, guaranteeing reliable performance.