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.
- 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.
-
Start a Decodo Web Advanced plan (free trials available) via dashboard.
-
Copy the auto-generated basic authentication token in the Web Advanced page.
- Open up your favourite MCP client and add the following configuration:
{
"Decodo": {
"url": "https://mcp.decodo.com/mcp",
"headers": {
"Authorization": "Basic <web_advanced_token>"
}
}
}
Details
- Node.js 18.0+
- An MCP client - popular choices are Claude Desktop and Cursor
- Clone this repository:
git clone https://github.com/Decodo/decodo-mcp-server
- Run the following commands in the terminal:
cd decodo-mcp-server
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:
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. |
reddit_post |
Scrapes a specific Reddit post for a given query, and returns parsed results. | Scrape the following Reddit post: https://www.reddit.com/r/horseracing/comments/1nsrn3/ |
reddit_subreddit |
Scrapes a specific Reddit subreddit for a given query, and returns parsed results. | Scrape the top 5 posts on r/Python this week. |
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. |
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.
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.
All code is released under the MIT License.