Face Generator MCP Server
フェイスジェネレーターMCPサーバー
https://thispersondoesnotexist.comを使用して人間の顔画像を生成するためのモデルコンテキストプロトコル (MCP) サーバー。
特徴
人間の顔画像を生成する
複数の出力形状: 正方形、円、角丸長方形
設定可能な画像サイズ
正方形以外の図形の透明な背景
複数の画像のバッチ生成
Related MCP server: Gemini MCP Image Generation Server
インストール
npm install @dasheck0/face-generator使用法
MCPサーバーとして
サーバーを起動します。
npx face-generatorMCP クライアントを通じて
generate_faceツールを使用します。
ツールパラメータ
outputDir: (必須) 画像を保存するディレクトリfileName: オプションのファイル名(デフォルトはタイムスタンプ)count: 生成する画像の数(デフォルト: 1)width: 画像の幅(ピクセル単位)(デフォルト: 256)height: 画像の高さ(ピクセル単位)(デフォルト: 256)shape: 画像の形状(正方形|円|丸型、デフォルト: 正方形)borderRadius: 丸みを帯びた形状の境界の半径(デフォルト: 32)
例
{
"outputDir": "./output",
"count": 3,
"width": 512,
"height": 512,
"shape": "circle"
}ライセンス
マサチューセッツ工科大学
Available Tools
1 toolgenerate_faceC
Generate and save a human face image
| Name | Required | Description | Default |
|---|---|---|---|
| outputDir | Yes | Directory to save the image | |
| fileName | No | Optional file name (defaults to timestamp) | 1767357705977.png |
| count | No | Number of images to generate (default: 1) | |
| width | No | Width of the image in pixels (default: 256) | |
| height | No | Height of the image in pixels (default: 256) | |
| shape | No | Image shape (square|circle|rounded, default: square) | square |
| borderRadius | No | Border radius for rounded shape (default: 32) | |
| returnImageContent | No | Return image as base64 encoded content instead of file path (default: false) |
TDQS
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. While 'generate and save' implies a write operation, it doesn't disclose important behavioral traits like whether this requires specific permissions, what happens if the output directory doesn't exist, whether generation is deterministic or random, or what happens on failure. For a tool that creates files with no annotation coverage, this represents a significant gap in transparency.
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 perfectly concise at just 6 words: 'Generate and save a human face image.' It's front-loaded with the core functionality, contains zero wasted words, and communicates the essential purpose efficiently. Every word earns its place in this minimal but complete statement of function.
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?
For a tool with 8 parameters, no annotations, and no output schema, the description is insufficiently complete. It doesn't explain what the tool returns (file paths? success indicators? error formats?), doesn't mention any constraints or requirements beyond what's in the parameter schema, and provides no context about the generation quality, source, or limitations. The agent would need to guess about important behavioral aspects.
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 provides no parameter information beyond what's already in the schema, which has 100% coverage with detailed descriptions for all 8 parameters. The baseline score of 3 reflects that the schema does the heavy lifting, and the description adds no additional parameter context, semantics, or examples beyond what's already documented in the structured schema fields.
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 and save a human face image' clearly states the verb ('generate and save') and resource ('human face image'), making the purpose immediately understandable. It's specific about generating a face image rather than generic image generation. However, without sibling tools to differentiate from, it can't achieve the full 5 points for sibling distinction.
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, prerequisites, or constraints. It simply states what the tool does without any context about appropriate use cases, limitations, or when other tools might be more suitable. This leaves the agent with insufficient context for optimal tool selection.
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
v1.0.0- Changed
generate_face1 field changed- changed
Input schema / properties / fileName / defaultPrevious value: -"1755515512893.png"New value: +"1767357705977.png"
1 tool update
- First observed
generate_face
TDQS
Scored across 1 tool
With only one tool, there is no possibility of ambiguity or overlap between tools. The single tool 'generate_face' has a clearly distinct and singular purpose.
The naming pattern cannot be inconsistent when there is only one tool. The tool name 'generate_face' follows a clear verb_noun convention, which would be consistent if more tools existed.
A single tool is generally too few for most MCP server purposes, as it provides minimal functionality and limits agent capabilities. For a face generation domain, one tool feels thin and under-scoped.
The tool set is severely incomplete for a face generation domain. While it covers generation, there are obvious gaps such as no tools for listing, retrieving, modifying, or deleting generated faces, creating dead ends for agents.
Maintenance
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