Asymptote를 사용하여 정밀한 기하학적 이미지를 생성하는 MCP(Model Context Protocol) 서버입니다. 이 서버는 MCP와 호환되는 AI 모델이 Asymptote 코드를 제공하여 이미지 생성을 요청할 수 있도록 합니다.
이 서버를 사용하기 전에 다음 사항이 설치되어 있는지 확인하세요.
Node.js : 16.x 이상 버전을 권장합니다. nodejs.org 에서 다운로드할 수 있습니다.
Asymptote : 이는 중요한 종속성입니다. asy 명령줄 도구가 설치되어 있어야 하며 시스템 PATH에 접근할 수 있어야 합니다.
다운로드와 자세한 설치 지침은 Asymptote 공식 웹사이트에서 확인하세요.
일반적인 설치 방법:
macOS(Homebrew를 통해): brew install asymptote
Debian/Ubuntu Linux: sudo apt-get install asymptote
Windows: MiKTeX 또는 TeX Live와 같은 TeX 배포판의 일부로 설치되는 경우가 많습니다. Asymptote bin 디렉터리가 PATH에 추가되었는지 확인하세요.
서버는 시작 시 asy -version 확인하고, 찾을 수 없으면 오류를 출력합니다.
Related MCP server: GGB Web MCP
이 패키지를 전역적으로 설치하려면( connect 명령을 직접 실행하려는 경우) 또는 다른 프로젝트의 종속성으로 설치하려면:
지엑스피1
저장소를 복제하고 개발을 위해 로컬로 실행하려면 다음을 수행합니다.
저장소를 복제합니다.
종속성 설치: npm install
프로젝트 빌드: npm run build
패키지가 설치되면(예: 전역 설치 또는 로컬 연결) 이 패키지에서 제공하는 connect 명령을 사용하여 MCP 서버를 시작할 수 있습니다. 이 명령은 MCP 클라이언트에서 호출하도록 설계되었습니다.
npx @luorivergoddess/mcp-geo connect또는 저장소를 복제하여 빌드한 경우:
node dist/cli.js서버는 @modelcontextprotocol/sdk 사용하여 stdin/stdout에서 JSON-RPC 메시지를 시작하고 수신합니다.
MCP 호환 클라이언트(예: Copilot Agent Mode가 있는 VS Code, Claude Desktop)를 이 서버를 사용하도록 구성하세요. 이 작업에는 일반적으로 클라이언트에게 서버 시작 방법을 알려주는 작업이 포함되는데, 이는 npx @luorivergoddess/mcp-geo connect 명령을 통해 수행됩니다.
renderGeometricImage서버는 하나의 주요 도구를 제공합니다.
이름: renderGeometricImage
설명: Asymptote 코드에서 이미지를 렌더링합니다.
입력 스키마:
{
"type": "object",
"properties": {
"asyCode": {
"type": "string",
"description": "A string containing complete and valid Asymptote code to be compiled. The server executes this code directly. Ensure necessary `import` statements (e.g., `import graph;`) and settings (e.g., `unitsize(1cm);`) are included within this code block if needed."
},
"outputParams": {
"type": "object",
"description": "Optional parameters to control the output image.",
"properties": {
"format": {
"type": "string",
"enum": ["svg", "png"],
"description": "The desired output image format. \"svg\" for scalable vector graphics (recommended for diagrams and plots), \"png\" for raster graphics. Defaults to \"svg\" if not specified."
},
"renderLevel": {
"type": "number",
"description": "For PNG output only. Specifies the rendering quality (supersampling level for antialiasing). Higher values (e.g., 4 or 8) produce smoother images but take longer to render and result in larger files. Asymptote default is 2. This server defaults to 4 if not specified and format is \"png\". Ignored for SVG output."
}
}
}
},
"required": ["asyCode"]
}출력: 도구는 콘텐츠 부분의 배열을 포함하는 CallToolResult 반환합니다.
성공하면 다음이 포함된 ImageContent 부분이 포함됩니다.
type: "image"
mimeType: "image/svg+xml" 또는 "image/png"
data: "<base64_encoded_image_data>"
또한 Asymptote의 로그가 포함된 TextContent 부분이 포함될 수도 있습니다.
오류가 발생하면 McpError 가 발생합니다.
renderGeometricImage 호출 예( arguments 필드에 대한 JSON):
{
"asyCode": "draw(unitsquare); fill(unitsquare, lightblue);",
"outputParams": {
"format": "png",
"renderLevel": 4
}
}클라이언트 호환성 참고 사항:
일부 MCP 클라이언트에는 지원되는 이미지 MIME 유형에 제한이 있을 수 있습니다.
예를 들어, image/svg+xml 지원하지 않는 클라이언트(예: 보고된 "Cherry Studio"의 특정 버전 또는 구성)에서 이 서버를 사용하는 경우, 도구 호출 인수에 "outputParams": { "format": "png" } 포함하여 png 형식을 요청해야 합니다. 형식을 지정하지 않으면 서버는 기본적으로 svg 를 사용합니다.
루오리버 여신
아이에스씨
Renders an image from Asymptote code.
| Name | Required | Description | Default |
|---|---|---|---|
| asyCode | Yes | A string containing complete and valid Asymptote code to be compiled. The server executes this code directly. Ensure necessary `import` statements (e.g., `import graph;`) and settings (e.g., `unitsize(1cm);`) are included within this code block if needed. | |
| outputParams | No | Optional parameters to control the output image. |
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 discloses that the tool 'Renders an image' and implies execution of code, but it doesn't mention behavioral traits like error handling (e.g., what happens with invalid code), performance (e.g., rendering time), or output specifics (e.g., image dimensions). The description is minimal, leaving gaps in transparency for a code-execution tool.
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: 'Renders an image from Asymptote code.' It is front-loaded with the core purpose and has zero waste, making it highly concise and well-structured for its simplicity.
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 (code execution with optional parameters), no annotations, and no output schema, the description is incomplete. It doesn't cover output details (e.g., what the image looks like, error responses) or behavioral aspects. However, the schema provides good parameter documentation, so it's minimally adequate but lacks context for a tool that executes external code.
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 parameters thoroughly. The description adds no additional meaning beyond the schema's details on 'asyCode' and 'outputParams'. It doesn't explain parameter interactions or provide examples, so it meets the baseline for high schema coverage without compensating value.
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's purpose: 'Renders an image from Asymptote code.' It specifies the verb ('Renders') and resource ('image'), and while there are no sibling tools to distinguish from, the description is specific about the input type (Asymptote code). However, it doesn't mention what kind of image is produced (e.g., geometric diagrams, plots) beyond the Asymptote context, which could be more precise.
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, as there are no sibling tools mentioned. It lacks context about typical use cases (e.g., generating diagrams for documentation, creating plots) or prerequisites (e.g., needing valid Asymptote syntax). Without siblings, the score is based on the absence of any usage context or exclusions.
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.
renderGeometricImageScored across 1 tool
With only one tool, there is no possibility of ambiguity or overlap between tools. The single tool has a clearly defined purpose that cannot be confused with any other tool in the set.
Since there is only one tool, naming consistency is inherently perfect. The tool name 'renderGeometricImage' follows a clear verb_noun pattern, and there are no other tools to compare it against for inconsistency.
A single tool is too few for a server named 'MCP Geometry Server', which suggests a broader scope in geometry. This minimal set feels thin and incomplete, likely requiring agents to rely heavily on other servers for basic geometric operations.
The tool surface is severely incomplete for a geometry domain. It only provides image rendering from Asymptote code, missing obvious operations like calculating areas, distances, transformations, or generating geometric shapes, which are fundamental to geometry.
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