About Fetch MCP Server
Fetch MCP Server retrieves web pages and extracts their content for LLMs, including pages that need JavaScript rendering or resist basic scraping. It combines browser automation, OCR, HTML extraction, and document parsing, then scores results to select an output.
Key features
- 01Fetches URLs through browser automation
- 02Extracts text with OCR and layout detection
- 03Parses HTML, PDF, DOCX, and PPTX
- 04Converts extracted content to Markdown
- 05Scores and selects extraction results
- 06Supports raw HTML output
Use cases
- 01Retrieve content from JavaScript-rendered pages
- 02Extract readable text from page screenshots
- 03Read web-hosted PDF or Office documents
- 04Request a page's original HTML
MCP support
The server exposes a fetch tool to MCP clients, with a URL argument and an optional raw setting for returning HTML. It also provides a fetch prompt for extracting page content as Markdown.
Configuration
Local server config is the same for every client. Copy it into your client’s MCP config file.
Tools
Local servers expose tools only once running on your machine, and the maintainer has not provided a tool list.
Details
From the project READMEFetch MCP Server

A Model Context Protocol server that provides web content fetching capabilities using browser automation, OCR, and multiple extraction methods. This server enables LLMs to retrieve and process content from web pages, even those that require JavaScript rendering or use techniques that prevent simple scraping.
Available Tools
fetch- Fetches a URL from the internet using browser automation and multi-method extraction (including OCR).url(string, required): URL to fetchraw(boolean, optional): Get the actual HTML content if the requested page, without simplification (default: false)
The server uses multiple methods to extract content:
- Browser automation with undetected-chromedriver
- OCR using pytesseract with layout detection
- HTML extraction using requests/BeautifulSoup
- Document parsing (PDF, DOCX, PPTX)
- Original markdown conversion method
The server uses a sophisticated scoring system to select the best result, considering:
-
Base content score (up to 50 points)
- Points awarded based on content length (1 point per 100 characters, max 50)
- Penalizes extremely short content (<100 characters)
-
Structure bonus (up to 20 points)
- Awards points for well-structured content with paragraphs
- More paragraphs indicate better content organization
-
Quality penalties
- Detects and penalizes error messages
- Reduces score for content containing error indicators
- Validates content structure and readability
The scoring system ensures the most reliable and high-quality content is selected, regardless of the extraction method used. Debug logging is available to track scoring decisions.
Prompts
- fetch
- Fetch a URL and extract its contents as markdown using browser automation
- Arguments:
url(string, required): URL to fetch
Installation
Using Docker
To install and run mcp-server-fetch using Docker, follow these steps:
-
Build the Docker image:
docker build -t mcp-server-fetch . -
Run the Docker container:
docker run --rm -i mcp-server-fetch
Configuration
Configure Roo Code or Claude App
Add to your Claude settings:
{
"mcpServers": {
"fetch": {
"command": "docker",
"args": [
"run",
"--rm",
"-i",
"mcp-server-fetch"
],
"disabled": false,
"alwaysAllow": []
}
}
}
Customization - User-agent
By default, depending on if the request came from the model (via a tool), or was user initiated (via a prompt), the server will use either the user-agent
ModelContextProtocol/1.0 (Autonomous; +https://github.com/modelcontextprotocol/servers)
or
ModelContextProtocol/1.0 (User-Specified; +https://github.com/modelcontextprotocol/servers)
This can be customized by adding the argument --user-agent=YourUserAgent to the args list in the configuration.
Browser Automation and OCR
The server now includes advanced content extraction capabilities:
- Automated handling of cookie consent banners
- Full-page screenshot capture
- OCR with layout detection using pytesseract
- Multiple extraction methods with automatic selection of best results
Contributing
We encourage contributions to help expand and improve mcp-server-fetch. Whether you want to add new tools, enhance existing functionality, or improve documentation, your input is valuable.
For examples of other MCP servers and implementation patterns, see: https://github.com/modelcontextprotocol/servers
Pull requests are welcome! Feel free to contribute new ideas, bug fixes, or enhancements to make mcp-server-fetch even more powerful and useful.
License
mcp-server-fetch is licensed under the MIT License. This means you are free to use, modify, and distribute the software, subject to the terms and conditions of the MIT License. For more details, please see the LICENSE file in the project repository.
Comments
New to MCP?
From what the protocol is to installing your first server, these answers cover most first questions.
An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.