Together AI MCP. Run open-source LLMs and ML tools in one place
Together AI connects your agent to hundreds of open-source LLMs for real-time inference, image generation, and model training. Use this MCP to generate vectors, run complex chats, or fine-tune models like Llama and Mixtral directly from any compatible client.
Claude 
ChatGPT 
Cursor 
Gemini 
Windsurf 
VS Code 
JetBrains 
Vercel Give Claude and any AI agent real-world access
Your agent handles multi-turn conversations using powerful open-source models by providing a simple chat history and requesting completion.
You can execute basic text generation tasks, giving the MCP a model ID and a prompt to get immediate textual output.
The MCP generates original images when you supply a detailed physical description (prompt) for an external diffusion model to use.
You can convert raw input texts into rich vector embeddings, which are ready to index in your analytical databases.
The MCP creates custom fine-tuning jobs using a base model and a specific dataset file, and you can track the status of those jobs.
You list all models available on the Together network to find the best engine for your NLP or vision task.
Ask an AI about this
Waiting for input…
What AI agents can do with Together AI with 7 Tools
These tools let you run model inference for chatting, text generation, image creation, embedding vectorization, and managing custom model training jobs.
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 MCPChat Completion
Executes a multi-turn conversation using specified Together AI models and message history.
Text Completion
Performs standard text generation by receiving only a model ID and an initial prompt.
Create Finetune Job
Initiates a new model fine-tuning job using a specified base model and training...
Generate Embeddings
Converts an array of input texts into rich vector embeddings for use in databases.
Generate Image
Creates a visual image by translating a detailed descriptive prompt into a picture...
List Finetune Jobs
Retrieves and shows the current status of all fine-tuning jobs you've created.
List Available Models
Lists every AI model currently supported on the Together AI network for your review.
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 Model Tooling
Today, integrating different AI functions means a lot of manual work. You run the chat in one window, then copy text out to a separate vector database UI to generate embeddings, and if you need an image, you have to switch over to an art generator's web portal. It’s a constant cycle of copying, pasting, and switching tabs just to get one feature working.
With this MCP, that manual handoff disappears. You tell your agent what you want—whether it’s generating vectors using `generate_embeddings` or getting text completions via `text_completion`—and the model runs everything internally. The result appears right where you asked for it.
Together AI: Model Operations
The specific manual steps that vanish include setting up separate API keys for different models and manually tracking job states across multiple vendor dashboards. You also stop having to decide if the model you are using is right for the task.
Now, your agent handles all of it. You simply ask the MCP to manage the workflow—for example, running `chat_completion` first, then asking it to summarize the output and generate embeddings with `generate_embeddings`. It’s one continuous flow.
What Together AI MCP does for your AI
Need to get bleeding-edge AI models into your daily workflow? This Together AI MCP connects your agent to an entire library of open-source LLMs. You can query powerful models—like Llama, Mixtral, and others—to run chats or perform basic text completions without leaving your chat environment. It's built for developers who need world-class inference speed right now.
Beyond just chatting, you can generate rich vector embeddings instantly from raw text logs to populate any analytical database. Need visuals? Instruct the MCP to create images using detailed descriptions. You can also provision and track custom fine-tuning jobs by pointing to a base model and a dataset file. Once connected via Vinkius, your agent gains access to this full suite of capabilities, letting you manage everything from basic text generation to complex model training cycles.
019d7613-8fef-713a-ac52-03cbd6e1202c 
How to set up Together AI MCP
The bottom line is, it lets your agent use advanced LLMs and ML tools without needing to switch environments or write complex boilerplate code.
Sign up for this integration and fetch a developer API key from the api.together.xyz control panel.
Plug that key into your agent, specifying which models you need access to.
Your AI client then executes sub-second serverless inference directly inside your command interface.
Who uses Together AI MCP
This MCP is for the AI Developer who needs production-grade model access right now. It's for engineers tired of juggling multiple APIs, switching between a chat window and a separate ML dashboard just to run one task.
Uses the MCP to bulk-generate vectors from raw logs or provisions custom training runs without leaving their main agent interface.
Tests open-source completions using alternative solutions, like Llama 3, natively in code editors alongside other development tasks.
Orchestrates fine-tuning parameters and launches compute jobs directly from their chat environment instead of needing a separate CLI or dashboard switch.
Benefits of connecting Together AI MCP
Stop switching between dashboards. You can generate embeddings or run chat completions using the generate_embeddings tool, all from your agent's prompt.
Manage entire model lifecycles—from initial testing to production fine-tuning. Use create_finetune_job and then check status with list_finetune_jobs without leaving your workflow.
Need a visual asset? Simply call generate_image by providing a detailed prompt; you get an image file back, not just text.
Explore the best model for any task. Use list_available_models to see hundreds of open-source options before running a single inference.
The chat_completion tool handles complex conversational flow, making your agent feel much more natural than simple prompt/response cycles.
Together AI MCP use cases
Building a Retrieval System
An engineer needs to index thousands of internal documents. Instead of writing a dedicated script, they ask their agent to use generate_embeddings on the raw text chunks and pipe those vectors directly into their vector store.
Creating Content for Marketing
A marketing specialist needs an illustration for a blog post. They prompt their agent, asking it to use generate_image with a detailed description (e.g., 'a futuristic cityscape at sunset'), and the image appears instantly.
Updating a Core Model
A machine learning engineer wants to adapt an open-source LLM for internal jargon. They use create_finetune_job with their base model ID and dataset, then monitor progress using list_finetune_jobs.
Testing Model Alternatives
A developer wants to compare Llama 3 against Mixtral for a chat feature. They use the agent's ability to run completions (chat_completion) multiple times in one session, comparing outputs side-by-side.
Together AI MCP tradeoffs
What to watch out for, and the recommended way to handle each one.
Hardcoding API Calls
Writing complex code blocks with explicit model endpoints and separate libraries just to run a single chat query or generate vectors.
Use the agent's built-in tools. Simply tell your agent to chat_completion after specifying the desired model ID, letting the MCP handle the boilerplate API connection.
Ignoring Model Variety
Assuming one powerful LLM is good enough for everything—using a single endpoint for chat, image generation, and embedding creation.
Use list_available_models to select the best specialized tool. For instance, use generate_embeddings instead of asking your main chat agent to do vector math.
Manual Job Tracking
After submitting a fine-tuning job, having to log into a separate web console every few minutes just to see if the process succeeded or failed.
Use list_finetune_jobs inside your agent. You submit the job with create_finetune_job, and then check its status within the same conversational thread.
When to use Together AI MCP
Use this MCP if your primary need is accessing a wide, current selection of open-source models for diverse tasks—chatting, embedding, image creation, or training. It’s ideal when you need to prototype quickly and test multiple model architectures in one place. Don't use it just because you want an LLM; the value here is in its breadth (the many available tools). If your goal is only simple text generation with a single provider and no other ML needs, another dedicated completion tool might suffice. But if you are building anything that requires data preparation (embeddings), visual assets (generate_image), or model customization (create_finetune_job), this MCP is necessary.
Frequently asked questions about Together AI MCP
How do I know what models are available using the Together AI MCP? +
You use the list_available_models tool. This instantly provides a list of all supported LLMs, letting you pick the best one for your chat or embedding task.
Can I fine-tune my own model with Together AI MCP? +
Yes. You start by calling create_finetune_job, providing a base model and your training data file, and then monitor the progress using list_finetune_jobs.
What is the difference between chat_completion and text_completion? +
Use chat_completion when you need multi-turn conversations that require a history of messages. Use text_completion for simple, single-shot prompts.
Does Together AI MCP handle image generation? +
Yes, it handles images using the generate_image tool. Just give it a detailed text description and receive an image asset back.
Is this only for coding tasks? +
No. While great for developers, you can use this MCP for anything that needs complex AI: data vectorization (generate_embeddings), content creation, or model training.