Mermaid MCP Server

A Model Context Protocol (MCP) server for validating Mermaid diagrams.

Implements a minimal Python wrapper over https://github.com/mermaid-js/mermaid-cli for simpler out of the box use.

Overview

Python MCP server for validating Mermaid diagrams and (optionally) rendering them as PNG images. It uses the Mermaid CLI tool to perform the validation and rendering.

The server provides LLMs with structured validation results including:

• Boolean validation status (is_valid: true/false) indicating whether the Mermaid diagram syntax is correct
• Detailed error messages explaining exactly what went wrong if validation fails (e.g., syntax errors, unsupported diagram types, malformed nodes)
• Optional base64-encoded PNG images of successfully rendered diagrams for visual verification

This enables LLMs to programmatically validate Mermaid diagram syntax, understand specific errors to provide helpful corrections, and optionally receive visual confirmation of the rendered output.

Also provides a simple Pydantic-AI MCP Client to invoke the MCP server using a Gemini model for testing.

Prerequisites

Important: This MCP server requires Node.js to be installed on your system, even if you're only using the server component (not the client). The server internally calls npx @mermaid-js/mermaid-cli as a subprocess to perform diagram validation and rendering.

Required Dependencies

• Node.js with npm (required for all usage)
• Mermaid CLI: Install with npm install -g @mermaid-js/mermaid-cli
• Python with uv (for running the MCP server)

Quick Dependency Setup

# Install Mermaid CLI globally
npm install -g @mermaid-js/mermaid-cli

# Verify installation
npx @mermaid-js/mermaid-cli --version

Quickstart

To use this server with an MCP client (like Claude Desktop), add the following configuration to your MCP settings:

Note: Make sure you have Node.js and Mermaid CLI installed (see Prerequisites above) before configuring the MCP server.

Configuration Format

1. Clone this repository

2. Add this to your MCP client configuration file (e.g., claude_desktop_config.json):

{
  "mcpServers": {
    "mermaid-validator": {
      "command": "uv",
      "args": ["run", "/path/to/mermaid_mcp_server.py"],
    }
  }
}

Configuration Options

• command: Use uv to run the server
• args: Run the server script with uv run
• cwd: Set to the absolute path of your cloned repository
• env: Environment variables for the server
  • MCP_TRANSPORT: Set to "stdio" for standard input/output communication

Example Extended Configuration

{
  "mcpServers": {
    "mermaid-validator": {
      "command": "uv", 
      "args": ["run", "/path/to/mermaid_mcp_server.py"],
      "env": {
        "MCP_TRANSPORT": "stdio",
      }
    }
  }
}

Abstractions Over Mermaid-CLI

The Python wrapper significantly simplifies the usage of the Mermaid CLI by abstracting away complex file handling and command-line arguments:

Without the wrapper (raw mermaid-cli):

# Create input file
echo "graph TD; A-->B" > diagram.mmd

# Create puppeteer config file
echo '{"args": ["--no-sandbox", "--disable-setuid-sandbox"]}' > puppeteer-config.json

# Run mermaid-cli with multiple arguments
npx @mermaid-js/mermaid-cli -i diagram.mmd -o output.png --puppeteerConfigFile puppeteer-config.json

# Handle output file and cleanup

With the Python wrapper:

# Simple function call with diagram text
result = await validate_mermaid_diagram("graph TD; A-->B")

# All file handling, configuration, and cleanup is automatic
# Returns structured result with validation status and base64-encoded image

Key Abstractions:

1. Temporary File Management: Automatically creates and cleans up temporary .mmd input files
2. Output File Handling: Manages temporary .png output files and converts them to base64 strings
3. Puppeteer Configuration: Automatically generates the required sandboxing configuration for headless browser rendering
4. Error Handling: Captures and returns structured error messages instead of raw stderr output
5. Command Construction: Builds the complete npx @mermaid-js/mermaid-cli command with all necessary flags
6. Resource Cleanup: Ensures all temporary files are properly deleted after processing

This abstraction allows users to focus on diagram validation and rendering without dealing with the underlying file system operations and command-line complexities.

Local Development

This repository can be used standalone to test the functionality of the Mermaid MCP validator programmatically.

Requirements

See the Prerequisites section above for required dependencies (Node.js, Mermaid CLI, and Python with uv).

Quick Setup (Recommended)

Use the provided Makefile for streamlined setup:

# Install all dependencies (Python + Node.js + Mermaid CLI)
make install

# Run validation tests
make test

Manual Setup

If you prefer manual setup:

1. Clone this repository
2. Install dependencies: uv sync
3. Install Mermaid CLI: npm install -g @mermaid-js/mermaid-cli
4. Copy .env.example to .env and fill in your API key
5. Run the server: uv run mermaid_mcp_server.py

Usage

The server exposes a tool for validating Mermaid diagrams:

• validate_mermaid_diagram: Validates a Mermaid diagram and returns validation results

Tool Parameters

• diagram_text (required): The Mermaid diagram text to validate
• return_image (optional, default: false): Whether to return the base64-encoded PNG image

Context Length Optimisation

Important: By default, the tool does not return the base64-encoded image (return_image=false) to preserve context length in LLM conversations. Base64-encoded images can be very long strings (often 10KB-100KB+) that significantly impact the available context for the conversation.

When to use each setting:

• return_image=false (default): Use for diagram validation only. Fast and context-efficient.
• return_image=true: Use only when you specifically need the rendered image data. Warning: This will consume significant context length.

Example Usage

# Validation only (recommended for most cases)
result = await validate_mermaid_diagram("graph TD; A-->B")
# Returns: MermaidValidationResult(is_valid=True, error_message=None, diagram_image=None)

# Validation with image (use sparingly)
result = await validate_mermaid_diagram("graph TD; A-->B", return_image=True)
# Returns: MermaidValidationResult(is_valid=True, error_message=None, diagram_image="iVBORw0KGgoAAAANSUhEUg...")

Testing

The project includes convenient testing commands:

# Run all tests
make test

# Or run the test script directly
uv run test_pydantic.py

The test script uses Pydantic AI with Gemini models to validate the MCP server functionality.