モデル コンテキスト プロトコル (MCP) を使用したカーソル用の Draw Things API 統合。
Node.js >= 14.0.0
http://127.0.0.1:7888で実行されている Draw Things API
Related MCP server: Jira Context MCP Server
# Install globally
npm install -g draw-things-mcp-cursor
# Or run directly
npx draw-things-mcp-cursorCursor でこのツールを設定するには、 cursor-setup.mdの詳細なガイドを参照してください。
クイックセットアップ:
~/.cursor/claude_desktop_config.jsonを作成または編集します。
{
"mcpServers": {
"draw-things": {
"command": "draw-things-mcp-cursor",
"args": []
}
}
}カーソルを再開
カーソルでの使用: generateImage({"prompt": "a cute cat"})
echo '{"prompt": "your prompt here"}' | npx draw-things-mcp-cursorprompt : 画像生成のためのテキストプロンプト（必須）
negative_prompt : 画像生成時の否定プロンプト
width : 画像の幅（デフォルト: 360）
height : 画像の高さ（デフォルト: 360）
steps : 生成ステップ数 (デフォルト: 8)
model : 生成に使用するモデル（デフォルト: "flux_1_schnell_q5p.ckpt"）
sampler : サンプリング方法（デフォルト: "DPM++ 2M AYS")
例：
echo '{
"prompt": "a happy smiling dog, professional photography",
"negative_prompt": "ugly, deformed, blurry",
"width": 360,
"height": 360,
"steps": 4
}' | npx draw-things-mcp-cursorCursor で MCP ツールとして使用する場合、ツールは次のパラメータを持つgenerateImageとして登録されます。
{
prompt: string; // Required - The prompt to generate the image from
negative_prompt?: string; // Optional - The negative prompt
width?: number; // Optional - Image width (default: 360)
height?: number; // Optional - Image height (default: 360)
model?: string; // Optional - Model name
steps?: number; // Optional - Number of steps (default: 8)
}生成された画像は、次のファイル名形式でimagesディレクトリに保存されます: <sanitized_prompt>_<timestamp>.png
成功：
{
"type": "success",
"content": [{
"type": "image",
"data": "base64 encoded image data",
"mimeType": "image/png"
}],
"metadata": {
"parameters": { ... }
}
}エラー：
{
"type": "error",
"error": "error message",
"code": 500
}問題が発生した場合:
http://127.0.0.1:7888で Draw Things API が実行されていることを確認します。
Cursor を使用する場合は~/.cursor/logsのログファイルを確認してください。
src/index.js に実行権限があることを確認します: chmod +x src/index.js
マサチューセッツ工科大学
Generate an image based on a prompt
| Name | Required | Description | Default |
|---|---|---|---|
| prompt | No | ||
| negative_prompt | No | ||
| width | No | ||
| height | No | ||
| steps | No | ||
| seed | No | ||
| guidance_scale | No | ||
| random_string | No |
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 only states the action ('Generate an image') without mentioning any behavioral traits like rate limits, authentication needs, output format, or potential side effects (e.g., resource usage). For a tool with 8 parameters and no annotation coverage, this is inadequate, as it leaves critical operational details unspecified.
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 extremely concise with a single sentence, 'Generate an image based on a prompt', which is front-loaded and wastes no words. It efficiently communicates the core function without unnecessary elaboration, making it easy to parse quickly. This is optimal for brevity, though it may sacrifice detail.
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 complexity (8 parameters, no annotations, no output schema), the description is incomplete. It doesn't explain the return values, error conditions, or how parameters interact, leaving significant gaps for the agent to operate effectively. While conciseness is high, it lacks the necessary context to handle the tool's full scope, making it insufficient for robust use.
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 meaning beyond the input schema, which has 8 parameters and 0% schema description coverage. Parameters like 'negative_prompt', 'steps', and 'guidance_scale' are undocumented in both the schema and description, leaving their purposes unclear. This fails to compensate for the low coverage, resulting in poor parameter understanding for the agent.
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 'Generate an image based on a prompt' clearly states the verb ('Generate') and resource ('image'), making the purpose understandable. However, it's somewhat vague about the specific method or technology (e.g., AI model type), and with no siblings, it doesn't need differentiation but lacks depth. This meets the minimum viable standard without being tautological or misleading.
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, such as scenarios for image generation, alternatives, or prerequisites. It merely states the basic function without context, leaving the agent to infer usage. This is a clear gap, as it doesn't help distinguish from potential implicit alternatives or set expectations.
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.
generateImageScored across 1 tool
With only one tool, there is no possibility of confusion or overlap between tools. The single tool 'generateImage' has a distinct and clear purpose, so disambiguation is perfect.
Since there is only one tool, naming consistency is inherently perfect. The tool name 'generateImage' follows a clear verb_noun pattern, and there are no other tools to compare it against for inconsistency.
A single tool for an image generation server feels thin and limited in scope. While it covers the core function, it lacks supporting operations like editing, listing, or managing images, making it borderline too few for a typical domain.
The server's purpose appears to be image generation, but with only a generate tool, there are significant gaps. There is no way to retrieve, update, delete, or manage generated images, which will likely cause agent failures in workflows requiring more than one-off generation.
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