@Leonfinn
基本信息
MCP server exposing Together AI inference endpoints (chat, image, vision, embeddings) as tools for Claude Desktop, Cursor, VS Code, and any other MCP-compatible client. Fixes silent empty-response bug in reasoning models.
使用下面的配置,将此服务器添加到你的 MCP 客户端。
{
"mcpServers": {
"together-ai": {
"command": "node",
"args": [
"/absolute/path/to/together-ai-mcp/index.js"
],
"env": {
"TOGETHER_API_KEY": "your_api_key_here",
"IMAGE_OUTPUT_DIR": "/path/to/save/images"
}
}
}
}工具列表暂未提供
当前收录暂时没有可展示的工具列表，不代表服务没有工具。请查阅项目文档了解支持的能力。
工具是从 README 中自动提取的。维护者可以在 ## Tools 标题下列出工具,即可填充这部分内容。
Together.ai MCP is a Node.js Model Context Protocol (MCP) server that exposes Together AI's inference endpoints — chat completions, image generation, vision, and embeddings — as tools callable from Claude Desktop, Cursor, VS Code, and any other MCP-compatible client.
Install Node.js 18+, obtain a Together AI API key, clone the repository, run npm install, copy .env.example to .env, add your API key, then configure the server in your MCP client (e.g., Claude Desktop) by adding a together-ai entry to the JSON config with the path to index.js and required environment variables.
fetch calls for vision and image generation to avoid SDK limitationsTogether AI’s reasoning models write chain-of-thought to non-standard fields (`reasoning_content`, `reasoning`) and exhaust the default 2048 token budget. This server raises `max_tokens` to 8192 and reads all three possible response fields so no model silently returns an empty answer.
The OpenAI SDK may silently omit `stream: false`, causing vision requests to fail with an opaque error. This server uses a raw `fetch` call with explicit `stream: false` and includes the full API error body in the exception.
Node.js 18+ and a Together AI API key. The server depends on `@modelcontextprotocol/sdk` and the `openai` package (used with Together AI’s base URL).
Images are saved as PNG files to the directory specified by the `IMAGE_OUTPUT_DIR` environment variable.
The server works with any model on Together AI’s serverless API. Models that require a dedicated endpoint (e.g., some Llama 4 variants) will return a 400 error. Use the correct tool for each model type: chat/reasoning models via `together_chat`, image via `together_generate_image`, vision via `together_vision`, and embeddings via `together_embed`.
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