What does the server validate?
It validates Mermaid diagram syntax using the Mermaid CLI. Returns `is_valid: true/false` and a detailed error message if validation fails.
@andrewginns
基本情報
Python MCP Server abstracting the official mermaid-cli for ease of use
以下の設定を使って、このサーバーを MCP 対応クライアントに追加してください。
{
"mcpServers": {
"mermaid-mcp-server-andrewginns": {
"command": "npx",
"args": [
"@mermaid-js/mermaid-cli",
"--version"
]
}
}
}ツール一覧はまだありません
この掲載には表示できるツール一覧がまだありません。サーバーにツールがないという意味ではありません。対応機能はプロジェクトのドキュメントをご確認ください。
上のボタンで一時的なサンドボックスでこのサーバーを実行し、ツール一覧を取得できます。
Mermaid MCP Server is a Model Context Protocol (MCP) server that validates Mermaid diagram syntax and optionally renders them as PNG images. It wraps the Mermaid CLI tool to provide LLMs with structured validation results, including boolean status, detailed error messages, and optional base64‑encoded images. Designed for developers integrating diagram validation into AI workflows.
Configure the server in your MCP client (e.g., Claude Desktop) using uv and the provided Python script. The server exposes a single tool validate_mermaid_diagram that accepts diagram_text (required) and return_image (optional, default false). Example configuration JSON is included in the README, and local testing can be done via make install and make test.
stdio transport for MCP clientsIt validates Mermaid diagram syntax using the Mermaid CLI. Returns `is_valid: true/false` and a detailed error message if validation fails.
Node.js with npm, Mermaid CLI (`npm install -g @mermaid-js/mermaid-cli`), and Python with `uv`. All are required even if only using the server component.
Returning images (base64 strings) can consume significant LLM context length (10KB–100KB+). Disabling images by default preserves context for longer conversations.
The server automatically creates and cleans up temporary `.mmd` and `.png` files, generates puppeteer configuration, and manages subprocess commands. No manual file management is needed.
Yes. Use `make install` to set up dependencies and `make test` to run validation tests. A test script using Pydantic AI with Gemini models is also provided.

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