MCP-Artisan

MCP-Artisan

A secure MCP server with a workspace sandbox for AI agents to manage prompts, save HTML, and render HTML to images.

Category
Visit Server

README

MCP-Artisan v2.0.0

A secure Model Context Protocol (MCP) tool with workspace sandbox for AI Agents to find prompts, save HTML, and render HTML to images.

🚨 Breaking Changes in v2.0.0

This is a major version update with breaking changes. All API parameters have been redesigned for enhanced security and usability.

Key Changes:

  • Workspace Sandbox: All operations are now restricted to a specified workspace directory
  • Simplified APIs: Removed complex path parameters, added intelligent file discovery
  • Enhanced Security: Built-in path validation prevents directory traversal attacks
  • Smart Organization: Automatic file organization in output/ subdirectories

Installation & Usage

# Install and run with workspace
npx mcp-artisan /path/to/your/workspace

# Example
npx mcp-artisan ./my-project

Required: You must specify a workspace directory when starting the server.

Configuration for AI Environments (Cursor, etc.)

To use MCP-Artisan with AI development environments like Cursor, you need to configure the MCP server command.

  1. Install MCP-Artisan globally or within your project:

    # Global installation
    npm install -g mcp-artisan
    
    # Or local project installation
    npm install mcp-artisan
    
  2. In your environment's MCP server settings (e.g., in .cursor-agent/mcp.json or a similar configuration file), set up the server command as follows:

    • Command: npx
    • Arguments: ["mcp-artisan", "/path/to/your/project/workspace"]

    Replace /path/to/your/project/workspace with the absolute path to the folder you want the AI to work in. This folder will be used to find prompts and save output files.

    Example Configuration (.cursor-agent/mcp.json):

    {
      "mcpServers": {
        "my-artisan-tool": {
          "command": "npx",
          "args": [
            "-y",
            "mcp-artisan",
            "/Users/username/my-ai-project"
          ]
        }
      }
    }
    

New API Reference

listPrompts

Lists available prompt files automatically.

  • Parameters: None
  • Behavior: Searches in workspace/prompts/ first, falls back to workspace root
  • Returns: Array of prompt filenames

getPromptContent

Reads prompt file content by filename.

  • Parameters:
    • promptFileName (string): Just the filename, no path needed
  • Behavior: Automatically searches in prompts directory and workspace root

saveHtml

Saves HTML with automatic organization.

  • Parameters:
    • htmlContent (string): Complete HTML content
    • subfolderName (string, max 20 chars): Descriptive folder name
    • fileName (string): Filename without .html extension
  • Output: Saves to workspace/output/{subfolderName}/{fileName}.html

renderImageFromHtml

Renders HTML to image using relative paths.

  • Parameters:
    • htmlPath (string): Relative path from workspace (e.g., "output/my-card/index.html")
    • imageType (optional): 'png' or 'jpeg'
  • Output: Image saved alongside HTML file

Migration from v1.x

v1.x Parameter v2.x Parameter Notes
promptsPath (removed) Auto-discovery in workspace
promptPath promptFileName Just filename, no path
outputPath subfolderName Auto-organized in output/
htmlPath (absolute) htmlPath (relative) Relative to workspace

Security Features

  • Workspace Sandbox: All file operations restricted to workspace
  • Path Validation: Prevents directory traversal (../) attacks
  • Input Sanitization: Validates filenames and folder names
  • Symlink Protection: Resolves real paths to prevent escapes

Example Workflow

# Start server with workspace
npx mcp-artisan ./my-project

# Directory structure will be:
# my-project/
# ├── prompts/           # Your prompt files
# │   ├── card.txt
# │   └── banner.md
# └── output/            # Generated files
#     └── my-cards/      # Organized by subfolder
#         ├── card.html
#         └── card.png

Development

# Clone and install
git clone <repo>
cd mcp-artisan
npm install

# Build
npm run build

# Development with workspace
npm run dev -- ./test-workspace

License

MIT

Contributing

Issues and pull requests are welcome. Please ensure all tests pass before submitting.

Recommended Servers

playwright-mcp

playwright-mcp

A Model Context Protocol server that enables LLMs to interact with web pages through structured accessibility snapshots without requiring vision models or screenshots.

Official
Featured
TypeScript
Magic Component Platform (MCP)

Magic Component Platform (MCP)

An AI-powered tool that generates modern UI components from natural language descriptions, integrating with popular IDEs to streamline UI development workflow.

Official
Featured
Local
TypeScript
Audiense Insights MCP Server

Audiense Insights MCP Server

Enables interaction with Audiense Insights accounts via the Model Context Protocol, facilitating the extraction and analysis of marketing insights and audience data including demographics, behavior, and influencer engagement.

Official
Featured
Local
TypeScript
VeyraX MCP

VeyraX MCP

Single MCP tool to connect all your favorite tools: Gmail, Calendar and 40 more.

Official
Featured
Local
graphlit-mcp-server

graphlit-mcp-server

The Model Context Protocol (MCP) Server enables integration between MCP clients and the Graphlit service. Ingest anything from Slack to Gmail to podcast feeds, in addition to web crawling, into a Graphlit project - and then retrieve relevant contents from the MCP client.

Official
Featured
TypeScript
Kagi MCP Server

Kagi MCP Server

An MCP server that integrates Kagi search capabilities with Claude AI, enabling Claude to perform real-time web searches when answering questions that require up-to-date information.

Official
Featured
Python
Neon Database

Neon Database

MCP server for interacting with Neon Management API and databases

Official
Featured
E2B

E2B

Using MCP to run code via e2b.

Official
Featured
Exa Search

Exa Search

A Model Context Protocol (MCP) server lets AI assistants like Claude use the Exa AI Search API for web searches. This setup allows AI models to get real-time web information in a safe and controlled way.

Official
Featured
Qdrant Server

Qdrant Server

This repository is an example of how to create a MCP server for Qdrant, a vector search engine.

Official
Featured