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Image Generation MCP Server

by sarthakkimtani
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Remote

画像生成MCPサーバー

Together AI による高品質な画像をシームレスに生成する Model Context Protocol (MCP) サーバー。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

Related MCP server: MCP Replicate FLUX

インストール

クロードデスクトップ

  • MacOSの場合: ~/Library/Application\ Support/Claude/claude_desktop_config.json

  • Windows の場合: %APPDATA%/Claude/claude_desktop_config.json

{
  "mcpServers": {
    "image-gen": {
      "command": "uv",
      "args": ["--directory", "/ABSOLUTE/PATH/TO/image-gen/", "run", "image-gen"],
      "env": {
        "TOGETHER_AI_API_KEY": "<API KEY>"
      }
    }
  }
}

利用可能なツール

サーバーは 1 つのツールを実装します。

画像を生成する

指定されたテキスト プロンプトとオプションの寸法に基づいて画像を生成します。

入力スキーマ:

{
  "prompt": {
    "type": "string",
    "description": "A descriptive prompt for generating the image (e.g., 'a futuristic cityscape at sunset')"
  },
  "width": {
    "type": "integer",
    "description": "Width of the generated image in pixels (optional)"
  },
  "height": {
    "type": "integer",
    "description": "Height of the generated image in pixels (optional)"
  },
  "model": {
    "type": "string",
    "description": "The exact model name as it appears in Together AI. If incorrect, it will fallback to the default model (black-forest-labs/FLUX.1-schnell)."
  }
}

前提条件

  • Python 3.12以上

  • httpx

  • マクピー

貢献

貢献を歓迎します！貢献するには、次の手順に従ってください。

  1. リポジトリをフォークする

  2. 新しいブランチを作成する ( feature/my-new-feature )

  3. 変更をコミットする

  4. ブランチをフォークにプッシュする

  5. プルリクエストを開く

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 tool
generate_imageC

Generate an image based on the text prompt, model, and optional dimensions

ParametersJSON Schema
NameRequiredDescriptionDefault
heightNoOptional height for the image
modelYesThe exact model name as it appears in Together AI. If incorrect, it will fallback to the default model (black-forest-labs/FLUX.1-schnell).
promptYesThe text prompt for image generation
widthNoOptional width for the image

TDQS

C2.9/5.0
Behavior2/5

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions the action ('Generate an image') but lacks details on permissions, rate limits, output format, or error handling. For a generative tool with zero annotation coverage, this is insufficient.

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 a single, efficient sentence that front-loads the core purpose without unnecessary details. Every word contributes to understanding the tool's function, making it appropriately concise and well-structured.

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 of image generation, lack of annotations, and no output schema, the description is incomplete. It doesn't address behavioral aspects like response format, errors, or constraints, leaving significant gaps for the agent to infer usage.

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 parameters. The description adds minimal value by listing inputs ('text prompt, model, and optional dimensions') but doesn't provide additional context beyond what the schema already specifies, aligning with the baseline for high coverage.

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 tool's purpose: 'Generate an image based on the text prompt, model, and optional dimensions.' It specifies the verb ('Generate') and resource ('image'), and mentions key inputs. However, with no sibling tools, it doesn't need to differentiate from alternatives, so a 5 is not warranted.

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 lists inputs without context about appropriate scenarios or limitations, leaving the agent with minimal usage direction.

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 confusion or overlap between tools. The tool's purpose is clearly defined and distinct by default.

Naming Consistency5/5

A single tool inherently has perfect naming consistency, as there are no other tools to compare it against. The name 'generate_image' follows a clear verb_noun pattern.

Tool Count2/5

A single tool for an image generation server feels thin and limited in scope. While it covers the core functionality, it lacks related operations like listing models, retrieving generated images, or managing parameters, making it borderline too few for the domain.

Completeness2/5

The server is severely incomplete for image generation. It only offers generation without any supporting tools for model selection, image retrieval, history management, or parameter tuning, which are common in such domains, leading to potential dead ends for agents.

Maintenance

ActivityInactive
ResponsivenessNo issues

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