How to Use the Together AI MCP in Claude Code

Q: If I need to check model status using Together AI with Claude Code, what tool works best?

Use listfinetunejobs. This allows your script to reliably query all fine-tuning jobs. You can then write logic that checks the job state and exits gracefully if the model isn't ready.

Q: Can I list all available models using Together AI with Claude Code?

Yes. You run listavailablemodels. This command confirms which model IDs are active and ready for use in your script, helping you write more robust shell scripts.

Run Together AI models for CI/CD pipelines using Claude Code's headless terminal execution.

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Connect Together AI MCP to Claude Code

Create your Vinkius account to connect Together AI to Claude Code 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 Claude Code

Ask AI about this MCP

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Claude

Perplexity

Automated Multi-Step Scripting

The `chat_completion` tool allows you to execute multi-step logic in a script. Pass a model ID and an array of messages, treating the entire sequence like a state machine within your CI/CD job. This is perfect for pre-flight checks or complex logging analysis where the output of step one dictates the prompt for step two, all without needing manual intervention.

Monitoring Model Deployment Status

When you run large model jobs in a pipeline, you need to know when they're done. Use `list_finetune_jobs` to query the status of existing jobs. You only pass no arguments and get back the job list. This lets your script poll for completion status, allowing your deployment process to wait reliably until the model is ready for use.

Batch Data Embedding for Pipelines

For batch processing in a cron job, run `generate_embeddings`. You supply a model ID and an array of strings. This efficiently converts massive amounts of raw text into vector embeddings. This is critical for data pipelines that need to index large corpora—think indexing thousands of log files or user reports before the application starts.

Setup guide

Set up Together AI MCP in Claude Code

Prerequisites

Claude Code CLI installed (npm install -g @anthropic-ai/claude-code)
Active Vinkius subscription with a valid endpoint token

1

Run the add command

Open your terminal and run the command shown on the right. Replace [YOUR_TOKEN_HERE] with your endpoint token from cloud.vinkius.com. Use --scope user to make it available across all projects.
2

Verify the connection

Start a Claude Code session and type /mcp to list connected servers. You should see together-ai-mcp with a green status indicator.
3

Start using tools

Ask Claude Code something like "Check my latest Together AI transactions." It will automatically discover and invoke the available Together AI tools.

Terminal

claude mcp add --transport http together-ai-mcp https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp

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Prerequisites

Claude Code CLI installed
Active Vinkius subscription with a valid endpoint token

1

Open the config file

Create or edit .mcp.json in your project root for project-level scope, or ~/.claude.json for user-level scope.
2

Add the Together AI MCP

Paste the JSON snippet shown on the right into the mcpServers object. Replace [YOUR_TOKEN_HERE] with your endpoint token from cloud.vinkius.com.
3

Restart Claude Code

Start a new Claude Code session. Type /mcp to confirm the server is connected. The tools will be automatically available in your conversation.

.mcp.json

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

Get your connection token →

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Common questions about Together AI MCP in Claude Code

You connect via a simple command and then execute tools like `chat_completion`. Since it's headless, the output pipes directly to your build logs or next stage of the workflow. It keeps everything contained in the terminal.

Use `list_finetune_jobs`. This allows your script to reliably query all fine-tuning jobs. You can then write logic that checks the job state and exits gracefully if the model isn't ready.

This MCP Server touches text prompts, structured log messages, model IDs, and training file IDs. All execution happens in a controlled terminal environment, ensuring that sensitive process data is handled securely.

Yes. You run `list_available_models`. This command confirms which model IDs are active and ready for use in your script, helping you write more robust shell scripts.

For quick testing or logging analysis within a pipeline, you can use `chat_completion`. You just provide the model ID and the specific messages array required for your test case.

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