INDIVIDUAL MCP TOOL

together_start_endpoint

Start a stopped dedicated endpoint.

usefulapi.ionone authenticationAvailability not checked

LIVE ENDPOINT

https://together-ai.usefulapi.io/mcp

No auth detected

Connect to this endpoint to inspect the live schema for together_start_endpoint and invoke it with your own arguments.

{}

Inferred read-only · medium confidence · heuristic, not a guarantee.

No write-capable action terms were found; this is not proof that invocation has no side effects.

CONNECT WITH APPROVAL

Review this server and its permissions before adding it. Secret placeholders must be set locally.

Codex

~/.codex/config.toml

[mcp_servers.together-ai-mcp]
url = "https://together-ai.usefulapi.io/mcp"
enabled = true

Claude Code

.mcp.json

{
  "mcpServers": {
    "together-ai-mcp": {
      "type": "http",
      "url": "https://together-ai.usefulapi.io/mcp"
    }
  }
}

Claude Desktop

Settings → Connectors → Add custom connector

Name: together-ai-mcp
Remote MCP URL: https://together-ai.usefulapi.io/mcp

Add this remote URL as a custom connector in Claude Desktop. Availability depends on the user plan and workspace policy.

Cursor

.cursor/mcp.json

{
  "mcpServers": {
    "together-ai-mcp": {
      "url": "https://together-ai.usefulapi.io/mcp"
    }
  }
}

Visual Studio Code

.vscode/mcp.json

Add to Visual Studio Code

{
  "servers": {
    "together-ai-mcp": {
      "type": "http",
      "url": "https://together-ai.usefulapi.io/mcp"
    }
  }
}

Generic MCP

Client-specific MCP configuration

{
  "name": "together-ai-mcp",
  "transport": "streamable-http",
  "url": "https://together-ai.usefulapi.io/mcp"
}

MCP Inspector

Run the official MCP Inspector locally and enter the indexed Streamable HTTP endpoint.

together_whoami — Identify the API key: its organization, project and project slug.
together_list_models — List Together's models with type (chat, language, code, image, embedding, moderation, rerank), context length, organization, license and per-token pricing.
together_list_files — List uploaded data files (fine-tune, eval and batch-api inputs, plus job outputs) with size, type, purpose and validation status.
together_get_file — Fetch one file's metadata, including its processing_status and validation_report (why a fine-tune training file was rejected).
together_list_fine_tunes — List fine-tuning jobs with status, base model, output model name and training settings.
together_get_fine_tune — Fetch one fine-tuning job: status, progress, hyperparameters, token counts, cost and the output model name.
together_list_fine_tune_events — List the event log of one fine-tuning job (queued, started, checkpoint saved, epoch completed, errors).
together_list_batches — List batch inference jobs with status, progress, model and input/output/error file ids.