Fetch MCP Server
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.
Available Tools
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)
Transport Modes
This server supports two transport modes:
- stdio (default): Standard input/output for local process communication
- http: HTTP-based transport using Streamable HTTP protocol with optional Bearer token authentication
Prompts
- fetch
- Fetch a URL and extract its contents as markdown
- Arguments:
url(string, required): URL to fetch
Installation
Optionally: Install node.js, this will cause the fetch server to use a different HTML simplifier that is more robust.
Using uv (recommended)
When using uv no specific installation is needed. We will
use uvx to directly run mcp-server-fetch.
Using PIP
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
Configuration
Running with HTTP Transport
To run the server with HTTP transport (Streamable HTTP protocol):
# Basic HTTP server (no authentication)
python -m mcp_server_fetch --transport http --port 8000
# With Bearer token authentication
python -m mcp_server_fetch --transport http --port 8000 --auth-token your-secret-token
# With custom host and port
python -m mcp_server_fetch --transport http --host 0.0.0.0 --port 3000 --auth-token your-secret-token
When using HTTP transport with authentication, clients must include the Bearer token in the Authorization header:
Authorization: Bearer your-secret-token
Security Note: When exposing the HTTP server beyond localhost, always use --auth-token to protect your server.
Configure for Claude.app
Add to your Claude settings:
Using uvx
{
"mcpServers": {
"fetch": {
"command": "uvx",
"args": ["mcp-server-fetch"]
}
}
}
Using docker
{
"mcpServers": {
"fetch": {
"command": "docker",
"args": ["run", "-i", "--rm", "mcp/fetch"]
}
}
}
Using pip installation
{
"mcpServers": {
"fetch": {
"command": "python",
"args": ["-m", "mcp_server_fetch"]
}
}
}
Configure for VS Code
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.
Using uvx
{
"mcp": {
"servers": {
"fetch": {
"command": "uvx",
"args": ["mcp-server-fetch"]
}
}
}
}
Using Docker
{
"mcp": {
"servers": {
"fetch": {
"command": "docker",
"args": ["run", "-i", "--rm", "mcp/fetch"]
}
}
}
}
Configure for HTTP Transport
When using HTTP transport, configure your MCP client to connect to the HTTP endpoint:
{
"mcpServers": {
"fetch": {
"url": "http://localhost:8000",
"transport": "http"
}
}
}
If using Bearer token authentication:
{
"mcpServers": {
"fetch": {
"url": "http://localhost:8000",
"transport": "http",
"headers": {
"Authorization": "Bearer your-secret-token"
}
}
}
}
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.
Customization - Proxy
The server can be configured to use a proxy by using the --proxy-url argument.
Windows Configuration
If you're experiencing timeout issues on Windows, you may need to set the PYTHONIOENCODING environment variable to ensure proper character encoding:
Windows configuration (uvx)
{
"mcpServers": {
"fetch": {
"command": "uvx",
"args": ["mcp-server-fetch"],
"env": {
"PYTHONIOENCODING": "utf-8"
}
}
}
}
Windows configuration (pip)
{
"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.
Debugging
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
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.