Together AI Image Server
Together AI 画像サーバー
英語 |简体中文
Together AI API を使用して画像を生成するための TypeScript ベースの MCP (Model Context Protocol) サーバー。
概要
このサーバーは、MCPプロトコルを介してTogether AIの画像生成モデルを使用して画像を生成するためのシンプルなインターフェースを提供します。これにより、Claudeやその他のMCP対応アシスタントは、テキストプロンプトに基づいて画像を生成できます。
Related MCP server: gemini-nano-banana-mcp
特徴
ツール
generate_image- テキストプロンプトから画像を生成する必須パラメータとしてテキストプロンプトを受け取ります
生成ステップと画像数を制御するためのオプションパラメータ
生成された画像へのURLとローカルパスを返します
前提条件
Node.js (v14以降を推奨)
Together AI APIキー
インストール
# Clone the repository
git clone https://github.com/zym9863/together-ai-image-server.git
cd together-ai-image-server
# Install dependencies
npm install構成
Together AI API キーを環境変数として設定します。
# On Linux/macOS
export TOGETHER_API_KEY="your-api-key-here"
# On Windows (Command Prompt)
set TOGETHER_API_KEY=your-api-key-here
# On Windows (PowerShell)
$env:TOGETHER_API_KEY="your-api-key-here"あるいは、プロジェクト ルートに.envファイルを作成することもできます。
TOGETHER_API_KEY=your-api-key-here発達
サーバーを構築します。
npm run build自動リビルドを使用した開発の場合:
npm run watchClaude Desktopでの使用
Claude Desktop で使用するには、サーバー設定を追加します。
macOSの場合: ~/Library/Application Support/Claude/claude_desktop_config.json
Windows の場合: %APPDATA%/Claude/claude_desktop_config.json
{
"mcpServers": {
"Together AI Image Server": {
"command": "/path/to/together-ai-image-server/build/index.js"
}
}
}/path/to/together-ai-image-server実際のインストールパスに置き換えます。
デバッグ
MCPサーバーはstdio経由で通信するため、デバッグが困難になる場合があります。パッケージスクリプトとして提供されているMCP Inspectorの使用をお勧めします。
npm run inspectorインスペクターは、ブラウザでデバッグ ツールにアクセスするための URL を提供します。
APIリファレンス
画像を生成する
Together AI の画像生成 API を使用して、テキスト プロンプトに基づいて画像を生成します。
パラメータ:
prompt(文字列、必須):画像生成のためのテキストプロンプトsteps(数値、オプション、デフォルト:4):拡散ステップ数(1~4)n(数値、オプション、デフォルト: 1): 生成する画像の数 (1-4)
戻り値:
以下を含む JSON オブジェクト:
image_urls: 生成された画像へのURLの配列local_paths: ローカルにキャッシュされた画像へのパスの配列
ライセンス
マサチューセッツ工科大学
貢献
貢献を歓迎します!お気軽にプルリクエストを送信してください。
Available Tools
1 toolgenerate_imageC
Generate image from text prompt using Together AI API
| Name | Required | Description | Default |
|---|---|---|---|
| n | No | Number of images to generate (default: 1, max: 4) | |
| steps | No | Number of diffusion steps (default: 4) | |
| prompt | Yes | Text prompt for image generation |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It only mentions the external API but does not disclose any behavioral traits such as rate limits, authentication needs, what happens under the hood, or potential side effects like image generation limits or API costs.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence that is concise and front-loaded with the core action. However, it is too short to cover necessary details, but for what it states, it is efficient.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the absence of an output schema and annotations, the description is incomplete. It provides no information about what the tool returns (e.g., image URLs or base64), any limitations, or error conditions. The user would need to guess or rely on external knowledge.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
All three parameters have descriptions in the input schema (100% coverage). The description adds no extra meaning beyond the schema, which already explains 'prompt', 'n', and 'steps'. Baseline 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: generating an image from a text prompt using the Together AI API. The verb 'generate' and resource 'image' are specific, and mentioning the API adds context. No siblings exist, so differentiation is not needed.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use or avoid this tool. There is no mention of prerequisites, alternatives, or when not to use it. The description simply states what it does without contextual usage advice.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
TDQS
With only one tool, there is no possibility of confusion between tools. The tool's purpose is clearly defined.
The single tool 'generate_image' follows a clear verb_noun pattern, which is consistent by default.
The server has only one tool, which is too few for a typical image generation service. Users would likely expect additional tools for model selection, image variants, or status retrieval.
The tool surface is severely incomplete; a comprehensive image generation server would typically include tools for listing models, configuring generation parameters, and possibly managing generated images.
Maintenance
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