A
licenseNot graded
qualityD
maintenanceImplements a Model Context Protocol (MCP) server powered by Flux API from Cephalon Cloud, enabling users to utilize advanced AI capabilities through standardized communication.
31
MIT
Image Generation MCP Server
by manascb1344
画像生成MCPサーバー
Together AIを介してFlux.1 Schnellモデルを用いた高品質な画像をシームレスに生成できるモデルコンテキストプロトコル（MCP）サーバー。このサーバーは、画像生成パラメータを指定するための標準化されたインターフェースを提供します。
特徴
Flux.1 Schnellモデルを活用した高品質画像生成
カスタマイズ可能な寸法（幅と高さ）のサポート
プロンプト検証と API の問題に対する明確なエラー処理
MCP対応クライアントとの簡単な統合
オプションでPNG形式でディスクに画像を保存
Related MCP server: Image Generation MCP Server
インストール
npm install together-mcpまたは直接実行します:
npx together-mcp@latest構成
MCP サーバー構成に追加します:
{
"mcpServers": {
"together-image-gen": {
"command": "npx",
"args": ["together-mcp@latest -y"],
"env": {
"TOGETHER_API_KEY": "<API KEY>"
}
}
}
}使用法
サーバーは1つのツールを提供します: generate_image
generate_image の使用
このツールには必須パラメータが1つだけあります（プロンプト）。その他のパラメータはオプションであり、指定されていない場合は適切なデフォルト値が使用されます。
パラメータ
{
// Required
prompt: string; // Text description of the image to generate
// Optional with defaults
model?: string; // Default: "black-forest-labs/FLUX.1-schnell-Free"
width?: number; // Default: 1024 (min: 128, max: 2048)
height?: number; // Default: 768 (min: 128, max: 2048)
steps?: number; // Default: 1 (min: 1, max: 100)
n?: number; // Default: 1 (max: 4)
response_format?: string; // Default: "b64_json" (options: ["b64_json", "url"])
image_path?: string; // Optional: Path to save the generated image as PNG
}最小限のリクエストの例
プロンプトのみが必要です:
{
"name": "generate_image",
"arguments": {
"prompt": "A serene mountain landscape at sunset"
}
}画像保存を含む完全なリクエスト例
デフォルトを上書きし、画像を保存するパスを指定します。
{
"name": "generate_image",
"arguments": {
"prompt": "A serene mountain landscape at sunset",
"width": 1024,
"height": 768,
"steps": 20,
"n": 1,
"response_format": "b64_json",
"model": "black-forest-labs/FLUX.1-schnell-Free",
"image_path": "/path/to/save/image.png"
}
}応答フォーマット
応答は、次の内容を含む JSON オブジェクトになります。
{
"id": string, // Generation ID
"model": string, // Model used
"object": "list",
"data": [
{
"timings": {
"inference": number // Time taken for inference
},
"index": number, // Image index
"b64_json": string // Base64 encoded image data (if response_format is "b64_json")
// OR
"url": string // URL to generated image (if response_format is "url")
}
]
}image_path が指定され、保存が成功した場合、応答には保存場所の確認が含まれます。
デフォルト値
リクエストで指定されていない場合は、次のデフォルトが使用されます。
モデル: "black-forest-labs/FLUX.1-schnell-Free"
幅: 1024
高さ: 768
手順: 1
n: 1
レスポンスフォーマット: "b64_json"
重要な注意事項
promptパラメータのみ必須ですすべてのオプションパラメータは、指定されていない場合はデフォルトを使用します。
パラメータを指定する場合、その制約（幅/高さの範囲など）を満たす必要があります。
Base64 応答はサイズが大きくなる可能性があります。大きな画像には URL 形式を使用してください。
画像を保存するときは、指定されたディレクトリが存在し、書き込み可能であることを確認してください。
前提条件
Node.js >= 16
Together AI APIキー
api.together.xyzにサインイン
APIキー設定に移動する
「作成」をクリックして新しいAPIキーを生成します
生成されたキーをコピーして、MCP 構成で使用します。
依存関係
{
"@modelcontextprotocol/sdk": "0.6.0",
"axios": "^1.6.7"
}発達
プロジェクトをクローンしてビルドします。
git clone https://github.com/manascb1344/together-mcp-server
cd together-mcp-server
npm install
npm run build利用可能なスクリプト
npm run build- TypeScriptプロジェクトをビルドするnpm run watch- 変更を監視して再構築するnpm run inspector- MCPインスペクターを実行する
貢献
貢献を歓迎します！以下の手順に従ってください。
リポジトリをフォークする
新しいブランチを作成する (
feature/my-new-feature)変更をコミットする
ブランチをフォークにプッシュする
プルリクエストを開く
機能リクエストやバグレポートはGitHub Issuesからご提出いただけます。新しいIssueを作成する前に、既存のIssueをご確認ください。
大幅な変更については、まず問題を開いて、提案する変更について話し合ってください。
ライセンス
このプロジェクトはMITライセンスの下で提供されています。詳細はLICENSEファイルをご覧ください。
Available Tools
1 toolgenerate_imageC
Generate an image using Together AI API
ParametersJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| prompt | Yes | Text prompt for image generation | |
| model | No | Model to use for generation (default: black-forest-labs/FLUX.1-schnell-Free) | |
| width | No | Image width (default: 1024) | |
| height | No | Image height (default: 768) | |
| steps | No | Number of inference steps (default: 1) | |
| n | No | Number of images to generate (default: 1) | |
| response_format | No | Response format (default: b64_json) | |
| image_path | No | Optional path to save the generated image as PNG |
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 the full burden of behavioral disclosure. It mentions the API provider but fails to describe critical behaviors like rate limits, authentication requirements, cost implications, error handling, or what happens when saving to 'image_path'. This leaves significant gaps for a tool with 8 parameters and no output schema.
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 with a single sentence that directly states the tool's purpose. There is zero wasted language, and it's front-loaded with the core functionality, making it highly efficient.
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?
Given the complexity (8 parameters, no output schema, no annotations), the description is insufficient. It doesn't explain return values, error cases, or behavioral nuances, leaving the agent with incomplete information for proper tool invocation in a real-world context.
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?
Schema description coverage is 100%, so the schema fully documents all 8 parameters. The description adds no additional parameter semantics beyond what's already in the schema, meeting the baseline score of 3 for high schema coverage without extra value.
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 action ('Generate an image') and the target resource ('using Together AI API'), providing a specific verb+resource combination. However, with no sibling tools mentioned, there's no explicit differentiation from alternatives, preventing a perfect score.
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 context for invocation. It simply states what the tool does without any usage instructions or exclusions.
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 tool update
v0.1.7- First observed
generate_image
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 has a clear and distinct purpose, making it impossible for an agent to misselect between multiple options.
Naming Consistency5/5
Since there is only one tool, naming consistency is inherently perfect. The tool name 'generate_image' follows a clear verb_noun pattern, and there are no other tools to compare it against for inconsistency.
Tool Count2/5
A single tool is too few for a server named 'Image Generation MCP Server', which suggests a broader scope. While the tool covers basic generation, the lack of additional tools (e.g., for editing, listing, or managing images) makes the surface feel thin and incomplete for the implied domain.
Completeness2/5
The server is severely incomplete for an image generation domain. It only provides a generate_image tool, with no coverage for related operations like listing generated images, editing parameters, deleting images, or handling variations. This creates significant gaps that will likely cause agent failures in broader workflows.
Maintenance
ActivityInactive
ResponsivenessNo issues
Resources
Unclaimed servers have limited discoverability.
Looking for Admin?
If you are the server author, to access and configure the admin panel.
Related MCP Connectors
MCP server for Flux AI image generation
Official FLUX MCP server. Generate, edit, vary, and browse images from Black Forest Labs.
A Model Context Protocol server for Wix AI tools
A comprehensive Model Context Protocol (MCP) server that enables AI assistants to interact with yo…
Related MCP Servers
- AlicenseBqualityDmaintenanceA Model Context Protocol server that enables generation of high-quality images using the Flux.1 Schnell model via Together AI, allowing users to create images from text prompts with customizable dimensions.118MIT
- FlicenseDqualityDmaintenanceA Model Context Protocol server that generates images using Replicate's FLUX model and stores them in Cloudflare R2, allowing users to create images through simple prompts and retrieve accessible URLs.111-
- AlicenseNot gradedqualityDmaintenanceA server that enables generating images through the Replicate API by calling the Flux Schnell model via the Model Context Protocol (MCP).3MIT