mcp-demo

# Search and Scrape MCP Server A Claude Desktop MCP server that provides web search and content scraping capabilities. This server enables Claude to search the web using the Serper API and scrape content from web pages. ## Features - **Web Search**: Search the web using Google Search results via Serper API - **Web Scraping**: Extract content and metadata from any web page - **Metadata Extraction**: Retrieve page titles, descriptions, keywords, and Open Graph metadata - **Error Handling**: Robust error handling for network issues and invalid responses - **SSL Security**: Secure HTTPS connections with proper certificate verification ## Prerequisites - Python 3.7+ - A Serper API key (get one at [serper.dev](https://serper.dev)) ## Installation 1. Clone the repository: ```bash git clone <repository-url> cd <repository-name> ``` 2. Create a virtual environment and activate it. I use uv to create and activate the virtual environment. Install it on macOS with Homebrew: ```bash brew install uv ``` Create and activate the virtual environment: ```bash uv venv ``` 3. Install dependencies using uv: ```bash uv pip install -r requirements.txt ``` 4. Create a `.env` file in the project root and add your Serper API key: ``` SERPER_API_KEY=your_api_key_here ``` ## Usage ### Use the server in Claude Desktop Edit the following file: claude_desktop_config.json (use Developer Settings in Claude Desktop to find this file) My file looks like this: ```json { "mcpServers": { "search": { "command": "<path-to-uv>", "args": [ "--directory", "<path-to-project>", "run", "main.py" ] } } } ``` ## Use the server in Cursor Use the following command when adding the server: ``` <path-to-uv> --directory <path-to-project> run main.py ``` ### Available Tools #### 1. Search Tool Searches the web using Serper API: ```python search(query: str) -> str ``` Example response: ```json [ { "title": "Search Result Title", "link": "https://example.com", "snippet": "Result description..." } ] ``` #### 2. Scrape Tool Scrapes content and metadata from a URL: ```python scrape(url: str, include_metadata: bool = True) -> Dict ``` Example response: ```json { "content": "Page content...", "url": "https://example.com", "status": 200, "metadata": { "title": "Page Title", "description": "Page description", "keywords": "key, words", "og_title": "OpenGraph Title", "og_description": "OpenGraph Description" } } ```