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URL Fetch MCP

URL Fetch MCP

by aelaguiz

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

Related MCP server: @kazuph/mcp-fetch

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 fetch

  • headers (optional): Additional headers to send with the request

  • timeout (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 from

  • timeout (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 from

  • headers (optional): Additional headers to send with the request

  • timeout (optional): Request timeout in seconds (default: 10)

Examples

The examples directory contains example scripts:

  • quick_test.py: Quick test of the MCP server

  • simple_usage.py: Example of using the client API

  • interactive_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

Available Tools

3 tools
fetch_imageC

Fetch an image from a URL and return it as an image.

This tool allows Claude to retrieve images from any accessible web URL.
The image is returned in a format that Claude can display.

Inputs: url (required), timeout. Returns: result

fetch_jsonA

Fetch JSON from a URL, parse it, and return it formatted.

This tool allows Claude to retrieve and parse JSON data from any accessible web URL.
The JSON is prettified for better readability.

Inputs: url (required), headers, timeout. Returns: result

fetch_urlC

Fetch content from a URL and return it as text.

This tool allows Claude to retrieve content from any accessible web URL.
The content is returned as text, making it suitable for HTML, plain text,
and other text-based content types.

Inputs: url (required), headers, timeout. Returns: result

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections.

Loading tool schema changelog…

    TDQS

    A3.5/5.0

    Scored across 3 tools

    Disambiguation5/5

    Each tool has a clearly distinct purpose: fetch_image retrieves images, fetch_json retrieves and parses JSON, and fetch_url retrieves general text content. The descriptions reinforce these distinctions, making it easy for an agent to select the right tool based on the expected response format.

    Naming Consistency5/5

    All tool names follow a consistent verb_noun pattern with 'fetch_' prefix and descriptive suffixes (image, json, url). This predictable naming scheme enhances usability and reduces cognitive load for agents.

    Tool Count4/5

    Three tools is a reasonable count for a URL fetching server, covering the main content types (images, JSON, text). It's slightly lean but well-scoped; adding tools for other formats like XML or binary data could improve completeness without being excessive.

    Completeness4/5

    The tools cover the core use cases for fetching web content: images, JSON, and general text. A minor gap exists for other structured data formats like XML or raw binary files, but agents can work around this by using fetch_url for text-based alternatives or requesting enhancements.

    Maintenance

    ActivityInactive
    ResponsivenessNo recent community issues

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