Cline向けの高度なModel Context Protocol（MCP）サーバー。EverArtのAIモデルと統合し、ベクター画像とラスター画像の両方を生成します。このサーバーは、柔軟なストレージオプションとフォーマット変換機能を備えた強力な画像生成機能を提供します。
ベクターグラフィックス生成
Recraft-Vector モデルを使用して SVG ベクター グラフィックを作成する
自動SVG最適化
ロゴ、アイコン、スケーラブルなグラフィックに最適
ラスターイメージ生成
PNG、JPEG、WebP形式のサポート
さまざまなスタイルに対応する複数の AI モデル
高品質な画像処理
柔軟なストレージ
カスタム出力パスとファイル名
自動ディレクトリ作成
フォーマット検証と拡張子の処理
Webプロジェクトの統合
Related MCP server: Gemini Image MCP
5000:FLUX1.1 : 標準品質、汎用画像生成
9000:FLUX1.1-ultra : 超高画質で詳細な画像を実現
6000:SD3.5 ：多様なスタイルに対応する安定した拡散3.5
7000:Recraft-Real :フォトリアリスティックスタイル
8000:Recraft-Vector :ベクターアートスタイル（SVG出力）
リポジトリをクローンします。
git clone https://github.com/nickbaumann98/everart-forge-mcp.git
cd everart-forge-mcp依存関係をインストールします:
npm installプロジェクトをビルドします。
npm run buildEverArt API キーを取得します:
EverArtに登録する
アカウント設定に移動します
APIキーを作成またはコピーする
Cline MCP 設定ファイルにサーバーを追加します。
VS Code拡張機能の場合:~/Library/Application Support/Code/User/globalStorage/saoudrizwan.claude-dev/settings/cline_mcp_settings.jsonを編集します:
{
"mcpServers": {
"everart-forge": {
"command": "node",
"args": ["/absolute/path/to/everart-forge-mcp/build/index.js"],
"env": {
"EVERART_API_KEY": "your_api_key_here"
},
"disabled": false,
"autoApprove": []
}
}
}Claude デスクトップ アプリの場合:~/Library/Application Support/Claude/claude_desktop_config.json (macOS) または OS の適切な場所を編集します。
Clineを再起動して新しいMCPサーバーをロードします
設定が完了すると、Cline を使用して次のようなプロンプトとともに画像を生成できるようになります。
「Recraft-Vector モデルを使用して、SVG 形式でミニマリストの技術ロゴを生成する」
「FLUX1.1-ultraモデルでフォトリアリスティックな風景画像を作成する」
「私のプロジェクトのために、人工知能を表すベクターアイコンを作成してください」
「プロフェッショナルな会社のロゴを SVG ファイルとして生成し、デスクトップに保存する」
サーバーは次のツールを提供します:
豊富なカスタマイズ オプションを使用して画像を生成します。
Parameters:
- prompt (required): Text description of desired image
- model: Model ID (5000:FLUX1.1, 9000:FLUX1.1-ultra, 6000:SD3.5, 7000:Recraft-Real, 8000:Recraft-Vector)
- format: Output format (svg, png, jpg, webp)
- output_path: Custom output path for the image
- web_project_path: Path to web project root for proper asset organization
- project_type: Web project type (react, vue, html, next, etc.)
- asset_path: Subdirectory within the web project assets
- image_count: Number of images to generate (1-10)注記:
SVG形式はRecraft-Vector（8000）モデルでのみ利用可能です。
デフォルトのフォーマットは、モデル8000の場合は「svg」、その他の場合は「png」です。
複合モデルIDを指定できます（例： "8000:Recraft-Vector"）
サーバーに保存されている、以前に生成されたすべてのイメージを一覧表示します。
デフォルトの画像ビューアで特定の画像を開きます。
Parameters:
- filename: Name of the image file to viewエラー: 無効なモデル ID : サポートされているモデル ID (5000、6000、7000、8000、9000) のいずれかを使用していることを確認してください
モデルと互換性のない形式: SVG 形式は、Recraft-Vector (8000) モデルでのみ使用できます。
画像が見つかりません: list_images ツールを使用して利用可能な画像を確認してください
API認証に失敗しました: EverArt APIキーを確認してください
画像が表示されない: ファイルの権限とパスを確認してください
MIT ライセンス - 詳細については LICENSE ファイルを参照してください。
Generate images using EverArt Models, optimized for web development. Supports web project paths, responsive formats, and inline preview. Available models:
5000:FLUX1.1: Standard quality
9000:FLUX1.1-ultra: Ultra high quality
6000:SD3.5: Stable Diffusion 3.5
7000:Recraft-Real: Photorealistic style
8000:Recraft-Vector: Vector art style (SVG format)
| Name | Required | Description | Default |
|---|---|---|---|
| prompt | Yes | Text description of desired image | |
| model | No | Model ID (5000:FLUX1.1, 9000:FLUX1.1-ultra, 6000:SD3.5, 7000:Recraft-Real, 8000:Recraft-Vector) | 5000 |
| format | No | Output format (svg, png, jpg, webp). Note: Vector format (svg) is only available with Recraft-Vector (8000) model. | svg |
| output_path | No | Optional: Custom output path for the generated image. If not provided, image will be saved in the default storage directory. | |
| web_project_path | No | Path to web project root folder for storing images in appropriate asset directories. | |
| project_type | No | Web project type to determine appropriate asset directory structure (e.g., 'react', 'vue', 'html', 'next'). | |
| asset_path | No | Optional subdirectory within the web project's asset structure for storing generated images. | |
| image_count | No | Number of images to generate |
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 lists available models and hints at web project integration but fails to describe critical behaviors like authentication needs, rate limits, error handling, or what happens when images are generated (e.g., saved to disk, returned as data). For a complex 8-parameter tool with no annotation coverage, this is a significant gap.
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 efficiently structured with a clear opening sentence and a bulleted list of models. However, the model list could be more concise (e.g., by grouping similar models), and some sentences like 'Supports web project paths, responsive formats, and inline preview' are vague and could be tightened.
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 tool's complexity (8 parameters, no annotations, no output schema), the description is incomplete. It lacks details on return values (e.g., image URLs, file paths), error conditions, web development integration specifics, and behavioral constraints. For a generative tool with multiple parameters, this leaves significant gaps for an AI agent.
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?
Schema description coverage is 100%, so the schema already documents all 8 parameters thoroughly. The description adds some value by listing model options with quality/style notes, but it doesn't provide additional semantic context beyond what's in the schema (e.g., explaining 'web_project_path' integration or 'project_type' implications). Baseline 3 is appropriate when the schema does the heavy lifting.
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 generates images using EverArt Models and specifies it's optimized for web development, which provides a specific verb+resource. However, it doesn't explicitly distinguish this from sibling tools like 'list_images' or 'view_image' beyond the core generation function, 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.
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 like 'list_images' or 'view_image'. It mentions web development optimization but doesn't specify scenarios where this is preferred over other image-related tools or when not to use it, leaving usage context implied at best.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
List all stored images
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
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 states the action ('List all stored images') but doesn't describe what 'List' entails—such as whether it returns metadata, thumbnails, or full images; if there's pagination or filtering; or any performance or rate-limiting considerations. This leaves significant gaps in understanding the tool's behavior.
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 'List all stored images' is extremely concise—a single, clear sentence with no wasted words. It's front-loaded and efficiently communicates the core action without any fluff, making it easy for an agent to parse quickly.
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 tool's simplicity (0 parameters, no output schema, no annotations), the description is minimal but insufficient. It doesn't explain what 'List' returns (e.g., a list of image IDs, metadata, or URLs), which is critical for a tool with no output schema. For a basic list operation, more context on the return format is needed to be complete.
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?
The input schema has 0 parameters with 100% coverage, so the schema fully documents the lack of inputs. The description doesn't add parameter details beyond this, but since there are no parameters to explain, it doesn't need to compensate. A baseline of 4 is appropriate as the description doesn't contradict or add unnecessary information.
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 'List all stored images' clearly states the verb ('List') and resource ('stored images'), making the tool's purpose immediately understandable. However, it doesn't explicitly differentiate from sibling tools like 'view_image' (which might show a single image) or 'generate_image' (which creates new images), so it doesn't reach the highest score.
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?
The description provides no guidance on when to use this tool versus alternatives like 'view_image' or 'generate_image'. It doesn't mention any prerequisites, exclusions, or contextual factors that would help an agent choose between these tools, leaving the usage unclear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Open a stored image in the default image viewer
| Name | Required | Description | Default |
|---|---|---|---|
| filename | Yes | Name of the image file to view |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It states the tool opens an image in a viewer, implying a read-only operation, but lacks details on behavioral traits such as whether it requires specific permissions, if it launches an external application, or any error handling (e.g., if the file doesn't exist). This leaves gaps for safe agent invocation.
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, efficient sentence that directly states the tool's purpose without unnecessary words. It is appropriately sized and front-loaded, making it easy for an agent to parse quickly.
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 tool's simplicity (one parameter, no output schema), the description is minimal but incomplete. It lacks context on usage guidelines, behavioral transparency (e.g., what happens if the file is missing), and doesn't leverage the absence of annotations to compensate, making it inadequate for safe agent operation.
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?
The description adds no parameter semantics beyond the input schema, which has 100% coverage and fully documents the single 'filename' parameter. This meets the baseline of 3, as the schema handles the heavy lifting, but the description doesn't provide additional context like file format support or path specifications.
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 action ('Open') and resource ('stored image'), specifying it opens in the default image viewer. However, it doesn't explicitly differentiate from sibling tools like 'generate_image' (creates new images) or 'list_images' (lists existing images), which is a minor gap.
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?
The description provides no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites (e.g., the image must already exist), exclusions, or comparisons to sibling tools like 'generate_image' for creating images or 'list_images' for browsing available images.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
v1.0.0generate_imagelist_imagesview_imageScored across 3 tools
The three tools have clearly distinct purposes: generate_image creates new images, list_images enumerates stored images, and view_image opens a specific stored image. There is no overlap or ambiguity between these functions, making it easy for an agent to select the correct tool for each task.
All tool names follow a consistent verb_noun pattern with snake_case: generate_image, list_images, and view_image. This predictable naming scheme enhances readability and usability, with no deviations or mixed conventions.
With only three tools, the server feels thin for an image generation and management domain. While the tools cover basic operations (create, list, view), more advanced functionality like editing, deleting, or organizing images is missing, which could limit agent workflows in practice.
The tool surface is significantly incomplete for an image management server. There are notable gaps: no ability to delete, update, or organize images (e.g., tagging, moving), and no tools for managing the generation process (e.g., canceling jobs, checking status). This will likely cause agent failures when trying to perform common image-related tasks.
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