How to Use the Together AI MCP in Cline

Q: How does Together AI help with generating code using Cline?

Cline uses textcompletion to generate the raw code based on your prompt. It doesn't just suggest; it writes the function, allowing you to immediately review and stage it as a file change.

Q: If I need an image asset for my project using Together AI with Cline, which tool do I use?

You use generateimage. Simply provide the model ID and the descriptive prompt. This capability means you can build your UI components end-to-end without switching context or leaving VS Code.

Q: Can I check which models are available for my project using Cline with Together AI?

Yes, the listavailablemodels tool handles this. It returns a list of every model ID that's currently operational on the Together AI platform, letting you verify your dependencies.

Q: If I am building a complex feature and need help with embedding data using Cline with Together AI, what do I use?

You run generateembeddings. You provide the model ID and an array of strings. This lets you vectorize your project's documentation or API schemas so they can be indexed and referenced by other code components.

Build entire features using Together AI models directly inside VS Code with Cline's end-to-end task runner.

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Connect Together AI MCP to Cline

Create your Vinkius account to connect Together AI to Cline and route execution through our secure gateway. The platform manages server hosting, runtime updates, and security layers. Configuration requires no manual server provisioning.

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Setup Together AI with Cline

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Visual Asset Generation for UI Components

Need an icon or a background graphic for the dashboard component you just wrote? Use `generate_image`. You feed it a model ID and a descriptive prompt, and it returns a ready-to-use asset. This means your code doesn't have to wait for a separate designer. Cline can write the logic, generate the image, and stage everything in one flow.

Core Text Generation and Code Completion

The `text_completion` tool is the workhorse here. You provide it with a model ID and a prompt—it executes base text completion. Cline uses this to write functions, fill in boilerplate code, or generate API calls. It's how Cline writes that missing half of a utility function or generates necessary README content for the feature you just built.

Discovering Available Models and Services

Before writing any code that relies on a specific LLM, use `list_available_models`. This tool pulls all model IDs currently available via Together AI. You pass no arguments; it just lists what's running. This is crucial for planning: Cline can check dependencies and ensure the required models exist before starting its multi-step execution sequence.

Setup guide

Set up Together AI MCP in Cline

Prerequisites

VS Code with Cline extension installed
Active Vinkius subscription with a valid endpoint token

1

Open Cline MCP settings

Click the Cline icon in the VS Code sidebar to open the Cline panel. Then click the MCP Servers icon (server stack) at the top-right corner of the panel.
2

Add a remote server

Click "Remote Servers" at the top, then click "Add Remote MCP". In the Name field, type together-ai-mcp. In the URL field, paste your Vinkius endpoint: https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp. Get your token from cloud.vinkius.com.
3

Enable the server

After saving, the server appears in the Cline MCP panel. Toggle the switch to enable it. The status indicator turns green when the connection is live.
4

Start using tools

Return to the Cline chat and ask: "Check my latest Together AI refund status." Cline will discover the available tools and request your approval before invoking each one — giving you full control over every action.

Cline MCP Settings

{
  "mcpServers": {
    "together-ai-mcp": {
      "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
    }
  }
}

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Common questions about Together AI MCP in Cline

Cline uses `text_completion` to generate the raw code based on your prompt. It doesn't just suggest; it writes the function, allowing you to immediately review and stage it as a file change.

You use `generate_image`. Simply provide the model ID and the descriptive prompt. This capability means you can build your UI components end-to-end without switching context or leaving VS Code.

This MCP Server touches text prompts, generated code snippets, model IDs, and image descriptive prompts. All input is handled by Together AI's infrastructure, keeping your intellectual property secure as you build.

Yes, the `list_available_models` tool handles this. It returns a list of every model ID that's currently operational on the Together AI platform, letting you verify your dependencies.

You run `generate_embeddings`. You provide the model ID and an array of strings. This lets you vectorize your project's documentation or API schemas so they can be indexed and referenced by other code components.

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