Prysm Web Scraper MCP server

The Prysm MCP Server enables AI assistants to scrape web content with high accuracy and flexibility. It offers various scraping modes, content analysis, format options, and image support to give AI models better access to web information.

Installation

You can install the Prysm MCP Server using npm:

# Recommended: Install the LLM-optimized version
npm install -g @pinkpixel/prysm-mcp

# Or install the standard version
npm install -g prysm-mcp

You can also run the server directly with npx without installing:

npx @pinkpixel/prysm-mcp

Configuration

Basic Setup

Create a mcp.json file with the following configuration:

{
  "mcpServers": {
    "prysm-scraper": {
      "description": "Prysm web scraper with custom output directories",
      "command": "npx",
      "args": [
        "-y",
        "@pinkpixel/prysm-mcp"
      ],
      "env": {
        "PRYSM_OUTPUT_DIR": "${workspaceFolder}/scrape_results",
        "PRYSM_IMAGE_OUTPUT_DIR": "${workspaceFolder}/scrape_results/images"
      }
    }
  }
}

Output Directory Configuration

By default, when saving formatted results, files will be saved to ~/prysm-mcp/output/. You can customize this in several ways:

Using Environment Variables

# Linux/macOS
export PRYSM_OUTPUT_DIR="/path/to/custom/directory"
export PRYSM_IMAGE_OUTPUT_DIR="/path/to/custom/image/directory"

# Windows (Command Prompt)
set PRYSM_OUTPUT_DIR=C:\path\to\custom\directory
set PRYSM_IMAGE_OUTPUT_DIR=C:\path\to\custom\image\directory

# Windows (PowerShell)
$env:PRYSM_OUTPUT_DIR="C:\path\to\custom\directory"
$env:PRYSM_IMAGE_OUTPUT_DIR="C:\path\to\custom\image\directory"

Using Tool Parameters

You can specify output paths directly when calling the tools:

# For general results
Format the scraped data as markdown and save it to "/absolute/path/to/file.md"

# For image downloads when scraping
Please scrape https://example.com and download images to "/absolute/path/to/images"

Using the Scraping Tools

The server provides three scraping modes with different intensity levels:

Focused Scraping

Fast web scraping optimized for speed (fewer scrolls, main content only).

Please scrape https://example.com using the focused mode

Available Parameters:

• url (required): URL to scrape
• maxScrolls (optional): Maximum number of scroll attempts (default: 5)
• scrollDelay (optional): Delay between scrolls in ms (default: 1000)
• scrapeImages (optional): Whether to include images in results
• downloadImages (optional): Whether to download images locally
• maxImages (optional): Maximum images to extract
• output (optional): Output directory for downloaded images

Balanced Scraping

Balanced web scraping approach with good coverage and reasonable speed.

Please scrape https://example.com using the balanced mode

Available Parameters:

• Same as scrapeFocused with different defaults
• maxScrolls default: 10
• scrollDelay default: 2000
• Adds timeout parameter to limit total scraping time (default: 30000ms)

Deep Scraping

Maximum extraction web scraping (slower but thorough).

Please scrape https://example.com using the deep mode with maximum scrolls

Available Parameters:

• Same as scrapeFocused with different defaults
• maxScrolls default: 20
• scrollDelay default: 3000
• maxImages default: 100

Formatting Results

After scraping, you can format the data into different structured formats:

Format the scraped data as markdown

Available Parameters:

• data (required): The scraped data to format
• format (required): Output format - "markdown", "html", or "json"
• includeImages (optional): Whether to include images in output (default: true)
• output (optional): File path to save the formatted result

You can also save formatted results to a file by specifying an output path:

Format the scraped data as markdown and save it to "my-results/output.md"

Path Handling Rules

The formatResult tool handles paths in the following ways:

• Absolute paths: Used exactly as provided (/home/user/file.md)
• Relative paths: Saved relative to the configured output directory (subfolder/file.md)
• Filename only: Saved in the configured output directory (output.md)
• Directory path: If the path points to a directory, a filename is auto-generated based on content and timestamp

How to add this MCP server to Cursor

There are two ways to add an MCP server to Cursor. The most common way is to add the server globally in the ~/.cursor/mcp.json file so that it is available in all of your projects.

If you only need the server in a single project, you can add it to the project instead by creating or adding it to the .cursor/mcp.json file.

Adding an MCP server to Cursor globally

To add a global MCP server go to Cursor Settings > MCP and click "Add new global MCP server".

When you click that button the ~/.cursor/mcp.json file will be opened and you can add your server like this:

{
    "mcpServers": {
        "cursor-rules-mcp": {
            "command": "npx",
            "args": [
                "-y",
                "cursor-rules-mcp"
            ]
        }
    }
}

Adding an MCP server to a project

To add an MCP server to a project you can create a new .cursor/mcp.json file or add it to the existing one. This will look exactly the same as the global MCP server example above.

How to use the MCP server

Once the server is installed, you might need to head back to Settings > MCP and click the refresh button.

The Cursor agent will then be able to see the available tools the added MCP server has available and will call them when it needs to.

You can also explictly ask the agent to use the tool by mentioning the tool name and describing what the function does.