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qualityA Model Context Protocol (MCP) server for web research. Bring real-time info into Claude and easily research any topic.
Last updated -
3
15,704
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MIT License
Uses Pydantic for type validation of URL parameters and request configuration
URL Fetch MCP
A clean Model Context Protocol (MCP) implementation that enables Claude or any LLM to fetch content from URLs.
Features
Fetch content from any URL
Support for multiple content types (HTML, JSON, text, images)
Control over request parameters (headers, timeout)
Clean error handling
Works with both Claude Code and Claude Desktop
Repository Structure
url-fetch-mcp/
├── examples/ # Example scripts and usage demos
├── scripts/ # Helper scripts (installation, etc.)
├── src/
│ └── url_fetch_mcp/ # Main package code
│ ├── __init__.py
│ ├── __main__.py
│ ├── cli.py # Command-line interface
│ ├── fetch.py # URL fetching utilities
│ ├── main.py # Core MCP server implementation
│ └── utils.py # Helper utilities
├── LICENSE
├── pyproject.toml # Project configuration
├── README.md
└── url_fetcher.py # Standalone launcher for Claude Desktop
Installation
# Install from source
pip install -e .
# Install with development dependencies
pip install -e ".[dev]"
Usage
Running the Server
# Run with stdio transport (for Claude Code)
python -m url_fetch_mcp run
# Run with HTTP+SSE transport (for remote connections)
python -m url_fetch_mcp run --transport sse --port 8000
Installing in Claude Desktop
There are three ways to install in Claude Desktop:
Method 1: Direct installation
# Install the package
pip install -e .
# Install in Claude Desktop using the included script
mcp install url_fetcher.py -n "URL Fetcher"
The url_fetcher.py file contains:
#!/usr/bin/env python
"""
URL Fetcher MCP Server
This is a standalone script for launching the URL Fetch MCP server.
It's used for installing in Claude Desktop with the command:
mcp install url_fetcher.py -n "URL Fetcher"
"""
from url_fetch_mcp.main import app
if __name__ == "__main__":
app.run()
Method 2: Use the installer script
# Install the package
pip install -e .
# Run the installer script
python scripts/install_desktop.py
The scripts/install_desktop.py script:
#!/usr/bin/env python
import os
import sys
import tempfile
import subprocess
def install_desktop():
"""Install URL Fetch MCP in Claude Desktop."""
print("Installing URL Fetch MCP in Claude Desktop...")
# Create a temporary Python file that imports our module
temp_dir = tempfile.mkdtemp()
temp_file = os.path.join(temp_dir, "url_fetcher.py")
with open(temp_file, "w") as f:
f.write("""#!/usr/bin/env python
# URL Fetcher MCP Server
from url_fetch_mcp.main import app
if __name__ == "__main__":
app.run()
""")
# Make the file executable
os.chmod(temp_file, 0o755)
# Run the mcp install command with the file path
try:
cmd = ["mcp", "install", temp_file, "-n", "URL Fetcher"]
print(f"Running: {' '.join(cmd)}")
result = subprocess.run(cmd, check=True, text=True)
print("Installation successful!")
print("You can now use the URL Fetcher tool in Claude Desktop.")
return 0
except subprocess.CalledProcessError as e:
print(f"Error during installation: {str(e)}")
return 1
finally:
# Clean up temporary file
try:
os.unlink(temp_file)
os.rmdir(temp_dir)
except:
pass
if __name__ == "__main__":
sys.exit(install_desktop())
Method 3: Use CLI command
# Install the package
pip install -e .
# Install using the built-in CLI command
python -m url_fetch_mcp install-desktop
Core Implementation
The main MCP implementation is in src/url_fetch_mcp/main.py:
from typing import Annotated, Dict, Optional
import base64
import json
import httpx
from pydantic import AnyUrl, Field
from mcp.server.fastmcp import FastMCP, Context
# Create the MCP server
app = FastMCP(
name="URL Fetcher",
version="0.1.0",
description="A clean MCP implementation for fetching content from URLs",
)
@app.tool()
async def fetch_url(
url: Annotated[AnyUrl, Field(description="The URL to fetch")],
headers: Annotated[
Optional[Dict[str, str]], Field(description="Additional headers to send with the request")
] = None,
timeout: Annotated[int, Field(description="Request timeout in seconds")] = 10,
ctx: Context = None,
) -> str:
"""Fetch content from a URL and return it as text."""
# Implementation details...
@app.tool()
async def fetch_image(
url: Annotated[AnyUrl, Field(description="The URL to fetch the image from")],
timeout: Annotated[int, Field(description="Request timeout in seconds")] = 10,
ctx: Context = None,
) -> Dict:
"""Fetch an image from a URL and return it as an image."""
# Implementation details...
@app.tool()
async def fetch_json(
url: Annotated[AnyUrl, Field(description="The URL to fetch JSON from")],
headers: Annotated[
Optional[Dict[str, str]], Field(description="Additional headers to send with the request")
] = None,
timeout: Annotated[int, Field(description="Request timeout in seconds")] = 10,
ctx: Context = None,
) -> str:
"""Fetch JSON from a URL, parse it, and return it formatted."""
# Implementation details...
Tool Capabilities
fetch_url
Fetches content from a URL and returns it as text.
Parameters:
url(required): The URL to fetchheaders(optional): Additional headers to send with the requesttimeout(optional): Request timeout in seconds (default: 10)
fetch_image
Fetches an image from a URL and returns it as an image.
Parameters:
url(required): The URL to fetch the image fromtimeout(optional): Request timeout in seconds (default: 10)
fetch_json
Fetches JSON from a URL, parses it, and returns it formatted.
Parameters:
url(required): The URL to fetch JSON fromheaders(optional): Additional headers to send with the requesttimeout(optional): Request timeout in seconds (default: 10)
Examples
The examples directory contains example scripts:
quick_test.py: Quick test of the MCP serversimple_usage.py: Example of using the client APIinteractive_client.py: Interactive CLI for testing
# Example of fetching a URL
result = await session.call_tool("fetch_url", {
"url": "https://example.com"
})
# Example of fetching JSON data
result = await session.call_tool("fetch_json", {
"url": "https://api.example.com/data",
"headers": {"Authorization": "Bearer token"}
})
# Example of fetching an image
result = await session.call_tool("fetch_image", {
"url": "https://example.com/image.jpg"
})
Testing
To test basic functionality:
# Run a direct test of URL fetching
python direct_test.py
# Run a simplified test with the MCP server
python examples/quick_test.py
License
MIT
hybrid server
The server is able to function both locally and remotely, depending on the configuration or use case.
A Model Context Protocol (MCP) server that enables Claude or other LLMs to fetch content from URLs, supporting HTML, JSON, text, and images with configurable request parameters.
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