excalidraw-mcp-connector

name	excalidraw-mcp-connector
description	MCP connector for Excalidraw virtual whiteboard – enables AI-powered diagram generation, sketch creation, and collaborative drawing through Claude
license	MIT
tags	["creative","whiteboard","mcp","excalidraw","diagrams"]
difficulty	beginner
time_to_master	4-8 weeks
version	1.0.0

Excalidraw MCP Connector

Overview

This skill enables Claude to interact with Excalidraw through the Model Context Protocol (MCP). It provides a bridge between Claude's AI capabilities and Excalidraw's JSON File Format, allowing natural language control of Excalidraw operations, intelligent automation, and AI-powered assistance for Excalidraw workflows.

When to Use This Skill

• Architecture diagram generation from descriptions
• Flowchart and sequence diagram creation
• Wireframe and mockup generation
• Collaborative whiteboard session management
• Export to PNG/SVG with custom styling

Architecture

┌─────────────┐     ┌─────────────────┐     ┌──────────────────┐
│   Claude     │────▶│   MCP Server    │────▶│   Excalidraw       │
│   (Client)   │◀────│   (TypeScript)  │◀────│   (JSON File Form)│
└─────────────┘     └─────────────────┘     └──────────────────┘

Core Concepts

MCP Server Setup

The connector implements an MCP server that exposes Excalidraw operations as tools Claude can invoke. The server translates natural language intentions into JSON File Format calls.

Key Endpoints/Interfaces

excalidraw-json elements, appState, files, collaborators

Implementation

// Excalidraw MCP Server Implementation
import { McpServer } from "@modelcontextprotocol/sdk/server/mcp.js";
import { StdioServerTransport } from "@modelcontextprotocol/sdk/server/stdio.js";
import { z } from "zod";

const server = new McpServer({
  name: "excalidraw-mcp-connector",
  version: "1.0.0",
});

// Tool: List/Query Resources
server.tool(
  "list_resources",
  "List and query Excalidraw resources with optional filters",
  {
    query: z.string().optional().describe("Search query or filter"),
    limit: z.number().optional().describe("Max results to return"),
  },
  async ({ query, limit }) => {
    // Call Excalidraw JSON File Format
    const response = await fetch(`${BASE_URL}excalidraw-json elements`, {
      headers: { "Authorization": `Bearer ${API_KEY}` },
    });
    const data = await response.json();
    return {
      content: [{ type: "text", text: JSON.stringify(data, null, 2) }],
    };
  }
);

// Tool: Create Resource
server.tool(
  "create_resource",
  "Create a new resource in Excalidraw",
  {
    name: z.string().describe("Resource name"),
    config: z.object({}).passthrough().optional().describe("Resource configuration"),
  },
  async ({ name, config }) => {
    const response = await fetch(`${BASE_URL}excalidraw-json elements`, {
      method: "POST",
      headers: {
        "Authorization": `Bearer ${API_KEY}`,
        "Content-Type": "application/json",
      },
      body: JSON.stringify({ name, ...config }),
    });
    const data = await response.json();
    return {
      content: [{ type: "text", text: `Created: ${JSON.stringify(data)}` }],
    };
  }
);

// Tool: Analyze/Report
server.tool(
  "analyze",
  "AI-powered analysis of Excalidraw data",
  {
    type: z.string().describe("Analysis type"),
    timeframe: z.string().optional().describe("Time range for analysis"),
  },
  async ({ type, timeframe }) => {
    // Fetch data and provide AI analysis
    const response = await fetch(`${BASE_URL}excalidraw-json elements`, {
      headers: { "Authorization": `Bearer ${API_KEY}` },
    });
    const data = await response.json();
    return {
      content: [{ type: "text", text: JSON.stringify(data, null, 2) }],
    };
  }
);

// Start server
const transport = new StdioServerTransport();
await server.connect(transport);

Claude Desktop Configuration

{
  "mcpServers": {
    "excalidraw-mcp-connector": {
      "command": "node",
      "args": ["path/to/excalidraw-mcp-connector/index.js"],
      "env": {
        "EXCALIDRAW_API_KEY": "your-api-key",
        "EXCALIDRAW_BASE_URL": "https://your-instance-url"
      }
    }
  }
}

Best Practices

1. Authentication: Store API keys securely using environment variables; never hardcode credentials
2. Rate Limiting: Implement request throttling to respect Excalidraw API rate limits
3. Error Handling: Provide clear, actionable error messages for common failure scenarios
4. Pagination: Handle paginated responses for large datasets efficiently
5. Caching: Cache frequently accessed read-only data to reduce API calls
6. Security: Validate all inputs before passing to the Excalidraw API; sanitize outputs
7. Logging: Log all API interactions for debugging and audit purposes

Example Prompts

"Generate a system architecture diagram showing microservices, message queues, and databases"

Security Considerations

• All API credentials must be stored as environment variables
• Implement input validation and sanitization for all tool parameters
• Use HTTPS for all API communications
• Follow the principle of least privilege for API token permissions
• Audit log all write operations for compliance tracking

Resources

• Excalidraw Official Documentation
• MCP SDK Documentation: https://modelcontextprotocol.io
• MCP Server Examples: https://github.com/modelcontextprotocol/servers
• SkillGalaxy Repository: https://github.com/Sandeeprdy1729/skill_galaxy

Changelog

Version	Date	Changes
1.0.0	2026-04-01	Initial MCP connector skill

---

Part of SkillGalaxy - 10,000+ comprehensive skills for AI-assisted development.