MCP Geometry Server
@luorivergoddess/mcp-geo
Ein MCP-Server (Model Context Protocol) zur Generierung präziser geometrischer Bilder mit Asymptote. Dieser Server ermöglicht MCP-kompatiblen KI-Modellen die Anforderung der Bildgenerierung durch Bereitstellung von Asymptote-Code.
Voraussetzungen
Stellen Sie vor der Verwendung dieses Servers sicher, dass Folgendes installiert ist:
Node.js : Version 16.x oder höher wird empfohlen. Sie können es von nodejs.org herunterladen.
Asymptote : Dies ist eine kritische Abhängigkeit. Das Befehlszeilentool
asymuss installiert und im PATH Ihres Systems zugänglich sein.Besuchen Sie die offizielle Asymptote-Website zum Herunterladen und für ausführliche Installationsanweisungen.
Gängige Installationsmethoden:
macOS (über Homebrew):
brew install asymptoteDebian/Ubuntu Linux:
sudo apt-get install asymptoteWindows: Wird oft als Teil von TeX-Distributionen wie MiKTeX oder TeX Live installiert. Stellen Sie sicher, dass das Asymptote-
bin-Verzeichnis zu Ihrem Pfad hinzugefügt wird.
Der Server versucht beim Start, nach
asy -versionzu suchen und gibt einen Fehler aus, wenn sie nicht gefunden wird.
Related MCP server: GGB Web MCP
Installation
So installieren Sie dieses Paket global (wenn Sie den Befehl „ connect direkt ausführen möchten) oder als Abhängigkeit in einem anderen Projekt:
npm install @luorivergoddess/mcp-geoWenn Sie das Repository geklont haben und es lokal für die Entwicklung ausführen möchten:
Klonen Sie das Repository.
Abhängigkeiten installieren:
npm installErstellen Sie das Projekt:
npm run build
Verwendung
Starten des Servers
Sobald das Paket installiert ist (z. B. global oder lokal verknüpft), können Sie den MCP-Server mit dem in diesem Paket bereitgestellten connect -Befehl starten. Dieser Befehl ist für den Aufruf durch einen MCP-Client vorgesehen.
npx @luorivergoddess/mcp-geo connectOder, wenn Sie das Repository geklont und erstellt haben:
node dist/cli.jsDer Server wird gestartet und wartet unter Verwendung von @modelcontextprotocol/sdk auf JSON-RPC-Nachrichten auf stdin/stdout.
MCP-Client-Integration
Konfigurieren Sie Ihren MCP-kompatiblen Client (z. B. VS Code mit Copilot-Agent-Modus, Claude Desktop) für die Verwendung dieses Servers. Dazu müssen Sie dem Client in der Regel mitteilen, wie der Server gestartet werden soll. Dies geschieht mit dem Befehl npx @luorivergoddess/mcp-geo connect .
Verfügbares Tool: renderGeometricImage
Der Server stellt ein primäres Tool bereit:
Name:
renderGeometricImageBeschreibung: Rendert ein Bild aus Asymptote-Code.
Eingabeschema:
{ "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"] }Ausgabe: Das Tool gibt ein
CallToolResultzurück, das ein Array von Inhaltsteilen enthält.Bei Erfolg enthält es einen
ImageContentTeil mit:type: "image"mimeType: "image/svg+xml"oder"image/png"data: "<base64_encoded_image_data>"
Es kann auch einen
TextContentTeil mit Protokollen von Asymptote enthalten.Wenn ein Fehler auftritt, wird ein
McpErrorausgegeben.
Beispiel für renderGeometricImage Aufruf (JSON für das arguments ):
{
"asyCode": "draw(unitsquare); fill(unitsquare, lightblue);",
"outputParams": {
"format": "png",
"renderLevel": 4
}
}Hinweise zur Client-Kompatibilität:
Bei einigen MCP-Clients können Einschränkungen hinsichtlich der unterstützten Bild-MIME-Typen vorliegen.
Wenn Sie diesen Server beispielsweise mit einem Client verwenden, der
image/svg+xmlnicht unterstützt (z. B. bestimmte Versionen oder Konfigurationen von „Cherry Studio“, wie berichtet), stellen Sie bitte sicher, dass Sie daspngFormat anfordern, indem Sie"outputParams": { "format": "png" }in Ihren Tool-Aufrufargumenten angeben. Der Server verwendet standardmäßigsvg, wenn kein Format angegeben ist.
Autor
luorivergoddess
Lizenz
ISC
Available Tools
1 toolrenderGeometricImageB
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. |
TDQS
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.
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
1 tool update
- First observed
renderGeometricImage
TDQS
Scored 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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