Web content fetching and conversion for efficient LLM usage
The Fetch MCP Server enables your AI assistant to retrieve and process web content for use in conversations. It fetches URLs, converts HTML to markdown, and extracts content in a format optimized for LLM consumption.
Part of the official MCP reference servers, this server handles the common need to bring web content into AI workflows. It supports fetching HTML pages and converting them to clean markdown, as well as fetching raw text and JSON data.
Useful for any workflow where your AI assistant needs to read web pages, API responses, or online documentation.
Valid MCP server (2 strong, 1 medium validity signals). 7 known CVEs in dependencies (0 critical, 3 high severity) Imported from the Official MCP Registry. 1 finding(s) downgraded by scanner intelligence.
7 files analyzed · 9 issues found
Security scores are indicators to help you make informed decisions, not guarantees. Always review permissions before connecting any MCP server.
This plugin requests these system permissions. Most are normal for its category.
Add this to your MCP configuration file:
{
"mcpServers": {
"fetch": {
"env": {},
"args": [
"-y",
"@modelcontextprotocol/server-fetch"
],
"command": "npx"
}
}
}From the project's GitHub README.
A Model Context Protocol server that provides web content fetching capabilities. This server enables LLMs to retrieve and process content from web pages, converting HTML to markdown for easier consumption.
[!CAUTION] This server can access local/internal IP addresses and may represent a security risk. Exercise caution when using this MCP server to ensure this does not expose any sensitive data.
The fetch tool will truncate the response, but by using the start_index argument, you can specify where to start the content extraction. This lets models read a webpage in chunks, until they find the information they need.
fetch - Fetches a URL from the internet and extracts its contents as markdown.
url (string, required): URL to fetchmax_length (integer, optional): Maximum number of characters to return (default: 5000)start_index (integer, optional): Start content from this character index (default: 0)raw (boolean, optional): Get raw content without markdown conversion (default: false)url (string, required): URL to fetchOptionally: Install node.js, this will cause the fetch server to use a different HTML simplifier that is more robust.
When using uv no specific installation is needed. We will
use uvx to directly run mcp-server-fetch.
Alternatively you can install mcp-server-fetch via pip:
pip install mcp-server-fetch
After installation, you can run it as a script using:
python -m mcp_server_fetch
Add to your Claude settings:
{
"mcpServers": {
"fetch": {
"command": "uvx",
"args": ["mcp-server-fetch"]
}
}
}
{
"mcpServers": {
"fetch": {
"command": "docker",
"args": ["run", "-i", "--rm", "mcp/fetch"]
}
}
}
{
"mcpServers": {
"fetch": {
"command": "python",
"args": ["-m", "mcp_server_fetch"]
}
}
}
For quick installation, use one of the one-click install buttons below...
For manual installation, add the following JSON block to your User Settings (JSON) file in VS Code. You can do this by pressing Ctrl + Shift + P and typing Preferences: Open User Settings (JSON).
Optionally, you can add it to a file called .vscode/mcp.json in your workspace. This will allow you to share the configuration with others.
Note that the
mcpkey is needed when using themcp.jsonfile.
{
"mcp": {
"servers": {
"fetch": {
"command": "uvx",
"args": ["mcp-server-fetch"]
}
}
}
}
{
"mcp": {
"servers": {
"fetch": {
"command": "docker",
"args": ["run", "-i", "--rm", "mcp/fetch"]
}
}
}
}
By default, the server will obey a websites robots.txt file if the request came from the model (via a tool), but not if
the request was user initiated (via a prompt). This can be disabled by adding the argument --ignore-robots-txt to the
args list in the configuration.
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.
The server can be configured to use a proxy by using the --proxy-url argument.
If you're experiencing timeout issues on Windows, you may need to set the PYTHONIOENCODING environment variable to ensure proper character encoding:
{
"mcpServers": {
"fetch": {
"command": "uvx",
"args": ["mcp-server-fetch"],
"env": {
"PYTHONIOENCODING": "utf-8"
}
}
}
}
{
"mcpServers": {
"fetch": {
"command": "python",
"args": ["-m", "mcp_server_fetch"],
"env": {
"PYTHONIOENCODING": "utf-8"
}
}
}
}
This addresses character encoding issues that can cause the server to timeout on Windows systems.
You can use the MCP inspector to debug the server. For uvx installations:
npx @modelcontextprotocol/inspector uvx mcp-server-fetch
Or if you've installed the package in a specific directory or are developing on it:
cd path/to/servers/src/fetch
npx @modelcontextprotocol/inspector uv run mcp-server-fetch
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.
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.
Be the first to review this server!
by Modelcontextprotocol · Developer Tools
Read, search, and manipulate Git repositories programmatically
by Toleno · Developer Tools
Toleno Network MCP Server — Manage your Toleno mining account with Claude AI using natural language.
by mcp-marketplace · Developer Tools
Create, build, and publish Python MCP servers to PyPI — conversationally.
by Microsoft · Content & Media
Convert files (PDF, Word, Excel, images, audio) to Markdown for LLM consumption
by mcp-marketplace · Developer Tools
Scaffold, build, and publish TypeScript MCP servers to npm — conversationally
by mcp-marketplace · Finance
Free stock data and market news for any MCP-compatible AI assistant.