URL Fetch MCP

Integrations

  • Uses Pydantic for type validation of URL parameters and request configuration

MCP de obtención de URL

Una implementación limpia del Protocolo de Contexto de Modelo (MCP) que permite a Claude o cualquier LLM obtener contenido de las URL.

Características

  • Obtener contenido desde cualquier URL
  • Soporte para múltiples tipos de contenido (HTML, JSON, texto, imágenes)
  • Control sobre los parámetros de la solicitud (encabezados, tiempo de espera)
  • Manejo limpio de errores
  • Funciona con Claude Code y Claude Desktop

Estructura del repositorio

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

Instalación

# Install from source pip install -e . # Install with development dependencies pip install -e ".[dev]"

Uso

Ejecución del servidor

# 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

Instalación en Claude Desktop

Hay tres formas de instalar en Claude Desktop:

Método 1: Instalación directa
# Install the package pip install -e . # Install in Claude Desktop using the included script mcp install url_fetcher.py -n "URL Fetcher"

El archivo url_fetcher.py contiene:

#!/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()
Método 2: utilizar el script de instalación
# Install the package pip install -e . # Run the installer script python scripts/install_desktop.py

El scripts/install_desktop.py :

#!/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())
Método 3: utilizar el comando CLI
# Install the package pip install -e . # Install using the built-in CLI command python -m url_fetch_mcp install-desktop

Implementación básica

La implementación principal de MCP está en 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...

Capacidades de la herramienta

obtener_url

Obtiene contenido de una URL y lo devuelve como texto.

Parámetros:

  • url (obligatorio): La URL a buscar
  • headers (opcional): encabezados adicionales para enviar con la solicitud
  • timeout (opcional): tiempo de espera de la solicitud en segundos (predeterminado: 10)

obtener_imagen

Obtiene una imagen de una URL y la devuelve como una imagen.

Parámetros:

  • url (obligatorio): La URL desde donde obtener la imagen
  • timeout (opcional): tiempo de espera de la solicitud en segundos (predeterminado: 10)

obtener_json

Obtiene JSON de una URL, lo analiza y lo devuelve formateado.

Parámetros:

  • url (obligatorio): La URL desde la cual obtener el JSON
  • headers (opcional): encabezados adicionales para enviar con la solicitud
  • timeout (opcional): tiempo de espera de la solicitud en segundos (predeterminado: 10)

Ejemplos

El directorio examples contiene scripts de ejemplo:

  • quick_test.py : Prueba rápida del servidor MCP
  • simple_usage.py : Ejemplo de uso de la API del cliente
  • interactive_client.py : CLI interactiva para pruebas
# 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" })

Pruebas

Para probar la funcionalidad básica:

# Run a direct test of URL fetching python direct_test.py # Run a simplified test with the MCP server python examples/quick_test.py

Licencia

Instituto Tecnológico de Massachusetts (MIT)

-
security - not tested
A
license - permissive license
-
quality - not tested

hybrid server

The server is able to function both locally and remotely, depending on the configuration or use case.

Un servidor de Protocolo de Contexto de Modelo (MCP) que permite a Claude u otros LLM obtener contenido de URL, compatible con HTML, JSON, texto e imágenes con parámetros de solicitud configurables.

  1. Features
    1. Repository Structure
      1. Installation
        1. Usage
          1. Running the Server
          2. Installing in Claude Desktop
        2. Core Implementation
          1. Tool Capabilities
            1. fetch_url
            2. fetch_image
            3. fetch_json
          2. Examples
            1. Testing
              1. License

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