README - decodo-mcp-server by decodo

Decodo MCP Server

This repository provides a Model Context Protocol (MCP) server that connects LLMs and applications to Decodo's platform. The server facilitates integration between MCP-compatible clients and Decodo's services, streamlining access to our tools and capabilities.

Features

Easy web data access. Simplified retrieval of information from websites and online sources.
Geographic flexibility. Access content regardless of regional restrictions.
Enhanced privacy. Browse and collect data while maintaining anonymity.
Reliable scraping. Advanced techniques to avoid detection and blocks.
Simple integration. Seamless setup with popular MCP clients like Claude Desktop, Cursor, and Windsurf.

Quick start via Smithery (Recommended)

Visit the decodo-mcp-server page on Smithery, select your preferred MCP client and generate installation instructions.

Obtain Web Scraping API credentials

You'll need the Decodo Advanced Web Scraping API credentials, which you can get by starting a free trial on the dashboard. Once you have a plan activated, take a note of your generated username and password:

Running the MCP server locally (manual)

Prerequisites

Node.js 18.0+
An MCP client - popular choices are Claude Desktop and Cursor

Step-by-step guide

Clone this repository:

git clone https://github.com/Decodo/decodo-mcp-server

Run the following commands in the terminal:

npm install
npm run build

Take note of your build location:

cd build/
pwd

Adding index.js to the end of this directory, your build file location should look something like this:

/Users/your.user/projects/decodo-mcp/build/index.js

Update your MCP client with the server information:

Claude Desktop

Follow the guide here to find the setup file, then update claude_desktop_config.json to look like this:

{
  "mcpServers": {
    "decodo-mcp": {
      "command": "node",
      "args": ["/Users/your.user/projects/decodo-mcp/build/index.js"],
      "env": {
        "SCRAPER_API_USERNAME": "your_username",
        "SCRAPER_API_PASSWORD": "your_password"
      }
    }
  }
}

Cursor

For installation instructions, see the Cursor documentation.

Tools

The server exposes the following tools:

Tool	Description	Example prompt
`scrape_as_markdown`	Scrapes any target URL, expects a URL to be given via prompt. Returns results in Markdown.	Scrape peacock.com from a US IP address and tell me the pricing.
`google_search_parsed`	Scrapes Google Search for a given query, and returns parsed results.	Scrape Google Search for shoes and tell me the top position.
`amazon_search_parsed`	Scrapes Amazon Search for a given query, and returns parsed results.	Scrape Amazon Search for toothbrushes

Parameters

The following parameters are inferred from user prompts:

Parameter	Description
`jsRender`	Renders target URL in a headless browser.
`geo`	Sets the country from which the request will originate.
`locale`	Sets the locale of the request.
`tokenLimit`	Truncates the response content up to this limit. Useful if the context window is small.
`fullResponse`	Skips automatic truncation and returns full content. If context window is small, may throw warnings.

Examples

Scraping geo-restricted content

Query your AI agent with the following prompt:

Scrape peacock.com from a German IP address and tell me the pricing.

This prompt will say that peacock.com is geo-restricted. To bypass the geo-restriction:

Scrape peacock.com from a US IP address and tell me the pricing.

Limiting number of response tokens

If your agent has a small context window, the content returned from scraping will be automatically truncated, in order to avoid context-overflow. You can increase the number of tokens returned within your prompt:

Scrape hacker news, return 50k tokens.

If your agent has a big context window, tell it to return full content:

Scrape hacker news, return full content.

Related repositories

Web Scraping API

Google Maps scraper

Amazon scraper

License

All code is released under the MIT License.