Skrapr - MCP Server

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Skrapr is an intelligent web scraping tool that combines AI capabilities with browser automation to extract structured data from websites.

In short:

website url + JSON schema = structured website data

This tool is an alternative between http request tool (dump) and scraper tools (brute force).

How it works

flowchart LR
  
  agent["Your AI Agent"] l1@--> | scrape_with_schema | skrapr["Skrapr"]
  
  subgraph server [Skrapr MCP Server]

  skrapr l2@--> | use tool | playwright["Playwright MCP"]

  end

  playwright l3@--> |navigates| website["Website"]
  skrapr l4@--> | think with | openai["Azure OpenAI"]
  skrapr l5@-.-> | json data | agent

  classDef customer stroke:#00f, stroke-width: 2
  classDef external stroke:#63af3e,stroke-width: 2,stroke-dasharray: 4
  classDef group fill:#ffffa455, stroke:#ff7c7c, stroke-width: 2

  agent:::customer
  agent:::someclass
  website:::external
  openai:::external
  server:::group

  classDef animate stroke-dasharray: 20,stroke-dashoffset: 900,animation: dash 25s linear infinite;
  class l1 animate

  l2@{ animate: true }
  l3@{ animate: true }
  l4@{ animate: true }
  l5@{ animate: true }

Available tools

• scrape_with_schema :

Example

Once configured, you can use Skrapr's capabilities directly in any MCP client (Claude Desktop, n8n, librechat, ...).

For example, you can ask Claude:

Extract product information from all categories at https://example.com/products.

I want name, description and price for each product.

The model with create a similar schema and pass it to Skrapr :

{
    "type":"object",
    "properties":{
        "products":{
            "type":"array",
            "items":{
                "type":"object",
                "properties":{
                    "name":{
                        "type":"string"
                    },
                    "price":{
                        "type":"string"
                    },
                    "description":{
                        "type":"string"
                    }
                }
            }
        }
    }
}

Scrapr will navigate the website, page by page, until it has found all the data it needs.

After scraping, you will receive the following response :

{
    "products": [
        {
            "name": "Product 1",
            "price": "10",
            "description": "Description of product 1"
        },
        {
            "name": "Product 2",
            "price": "20",
            "description": "Description of product 2"
        }
    ]
}

Getting Started with MCP Host

For a typical MCP Host integration (Claude Desktop), here's how to set it up:

With .NET:

{
  "mcpServers": {
    "skrapr": {
      "command": "dotnet",
      "args": [
        "run",
        "--project",
        "path/to/Skrapr/Skrapr.csproj"
        "-e", "AzureOpenAi__ApiKey=your-api-key", 
        "-e", "AzureOpenAi__Endpoint=https://your-resource.openai.azure.com/", 
        "-e", "AzureOpenAi__DeploymentName=your-deployment-name", 
        "-e", "PlaywrightMcp__IsLocal=true", 
      ]
    }
  }
}

With Docker:

1. Build the Docker image:

docker build -t skrapr .

2. Run the container:

docker run \
        -p 80:80 \
        -e AzureOpenAi__ApiKey=your-api-key \
        -e AzureOpenAi__Endpoint=https://your-resource.openai.azure.com/ \
        -e AzureOpenAi__DeploymentName=your-deployment-name \
        -e PlaywrightMcp__IsLocal=true \
        skrapr

3. The MCP server will be available at `http://localhost:5000`.

4. You can configure your MCP Host

{
      "mcpServers": {
        "skrapr": {
          "url": "http://localhost:5000/sse"
        }
      }
    }

With Cloud version

I am hosting a Skrapr instance on Cloud with everything configured.

If you are interested to try it out, reach me here to get access to the beta.

{
      "mcpServers": {
        "skrapr": {
          "url": "https://[ask-for-url]/sse"
        }
      }
    }

Playwright configuration

Playwright can be executed locally or remotely.

Local

Setting environment variable `PlaywrightMcp__IsLocal=true` will make Playwright run locally.

You need to have Node.js installed to make it work.

It uses `npx @playwright/mcp` to run Playwright.

Remote

Setting environment variable `PlaywrightMcp__Endpoint` will use Playwright on a remote host.

Under the hood?

AI Agent

Skrapr uses an AI agent internally aiming to :

• Understand website structure and content
• Autonomously navigate across the website
• Make strategic navigation decisions to locate the requested information
• Produce a structured JSON result based on a provided JSON schema.

Technical stack

It uses:

• Microsoft's Semantic Kernel to build the agent
• Playwright MCP to expose tools for websites navigation
• Azure OpenAI for AI capabilities and data extraction according to user-defined JSON schemas

Thanks to MCP (Model Context Protocol), you can connect Skrapr to any agent whatever the language.

Why Skrapr?

When building a AI Agent, you may want the agent to extract easily information from websites.

Currently, ai models can't navigate websites natively.

Simple HTTP Request tools exist however there is few limitations :

• No javascript execution: cannot read dynamic content
• Information may not be present in the loaded page, but in a subpage

Scraper tools are also exists, but they :

• Load to much information (randomly scrape)
• Takes to much time to scrape