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Together AI Image MCP Server

by stefanskiasan
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JavaScript
Hybrid

Together AI 画像サーバー

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 MCP サーバー。

設定

  1. 依存関係をインストールします:

npm install

  1. サーバーを構築します。

npm run build

Related MCP server: MCP Image Generator

構成

1. Together AI APIキー

サーバーにはTogether AI APIキーが必要です。Together AIのプラットフォームから取得できます。

2. 傾斜構成

Cline MCP 設定ファイルにサーバーを追加します。

macOS/Linux の場合: ~/Library/Application Support/Windsurf/User/globalStorage/saoudrizwan.claude-dev/settings/cline_mcp_settings.json

{
  "mcpServers": {
    "togetherai-image": {
      "command": "node",
      "args": ["/path/to/togetherai-image-server/build/index.js"],
      "env": {
        "TOGETHER_API_KEY": "your-api-key-here"
      },
      "disabled": false,
      "autoApprove": []
    }
  }
}

Clineでの使用法

サーバーは、次のパラメータを持つgenerate_imageツールを提供します。

{
  prompt: string;           // Required: Text description of the image to generate
  model?: string;          // Optional: Model to use (default: 'black-forest-labs/FLUX.1.1-pro')
  width?: number;          // Optional: Image width in pixels (default: 1024)
  height?: number;         // Optional: Image height in pixels (default: 768)
  steps?: number;         // Optional: Number of inference steps (default: 28)
  n?: number;             // Optional: Number of images to generate (default: 1)
  outputDir?: string;     // Optional: Directory where images will be saved (default: './output')
}

使用例

Cline でサーバーを次のように使用できます。

Generate an image of a cat and save it to my desktop:

<use_mcp_tool>
<server_name>togeherai-image</server_name>
<tool_name>generate_image</tool_name>
<arguments>
{
  "prompt": "A cute cat sitting on a windowsill",
  "outputDir": "/Users/username/Desktop/generated-images"
}
</arguments>
</use_mcp_tool>

出力

ツールは次の内容を含む JSON 応答を返します。

  • filepath : 保存した画像へのフルパス

  • filename : 保存した画像ファイルの名前

  • dimensions : 画像の元の寸法と最終的な寸法

特徴

  • Together AIの最新FLUX.1.1-proモデルをサポート

  • 画像のサイズが256ピクセル未満の場合は自動的にサイズを変更します

  • サイズ変更時にアスペクト比を維持

  • 高品質のJPEG出力（品質90％）

  • 出力ディレクトリを再帰的に作成します

  • 画像のサイズを含む詳細なメタデータを返します

Available Tools

1 tool
generate_imageC

Generate an image using Together AI

ParametersJSON Schema
NameRequiredDescriptionDefault
promptYesText description of the image to generate
modelNoModel to use for generationblack-forest-labs/FLUX.1.1-pro
widthNoImage width in pixels
heightNoImage height in pixels
stepsNoNumber of inference steps
nNoNumber of images to generate
outputDirNoFull absolute path where images will be saved (e.g., /Users/username/Projects/myapp/src/assets)
formatNoOutput format for the generated imagespng

TDQS

C2.9/5.0
Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries full burden for behavioral disclosure. It mentions 'Generate an image' which implies a creation/write operation, but provides no information about permissions needed, rate limits, costs, whether it's idempotent, or what happens with the generated images (e.g., are they saved locally as indicated by outputDir?). This is a significant gap for a tool with potentially complex behavior.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is extremely concise - a single sentence that directly states the tool's purpose without any unnecessary words. It's perfectly front-loaded with the essential information, making it highly efficient for an agent to parse.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For an image generation tool with 8 parameters and no output schema, the description is insufficient. It doesn't explain what the tool returns (e.g., file paths, success status, error handling), doesn't mention the outputDir parameter's significance for file storage, and provides no behavioral context despite the complexity implied by multiple configuration parameters. The lack of annotations exacerbates these gaps.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema description coverage is 100%, so the schema already documents all 8 parameters thoroughly. The description adds no additional parameter information beyond what's in the schema, which is acceptable given the comprehensive schema documentation. This meets the baseline expectation when schema coverage is high.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the verb ('Generate') and resource ('image') with the service provider ('using Together AI'), making the purpose immediately understandable. However, with no sibling tools mentioned, it doesn't need to differentiate from alternatives, so it falls short of a perfect 5.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides no guidance on when to use this tool versus alternatives, prerequisites, or constraints. It simply states what the tool does without any context about appropriate use cases or limitations.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections.

  1. 1 tool updatev1.0.0
    • First observedgenerate_image
Deploy Server
One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

TDQS

B3.1/5.0

Scored across 1 tool

Disambiguation5/5

With only one tool, there is no possibility of ambiguity or overlap between tools. The single tool 'generate_image' has a clear and distinct purpose, making it impossible for an agent to misselect between non-existent alternatives.

Naming Consistency5/5

The naming pattern cannot be inconsistent with only one tool. The tool name 'generate_image' follows a verb_noun convention, which is straightforward and predictable, though there are no other tools to compare it against for consistency.

Tool Count2/5

A single tool for an image generation server feels thin and under-scoped. While it covers the core functionality, typical image generation APIs offer more operations (e.g., variations, edits, or style transfers), making this count borderline too few for the apparent domain.

Completeness2/5

The tool set is severely incomplete for an image generation domain. It only provides generation, with no obvious gaps to assess since there are no other tools, but the lack of common operations like editing, upscaling, or batch processing suggests significant coverage issues for agent workflows.

Maintenance

ActivityInactive
ResponsivenessNo issues

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