What dependencies does the server require?
Docker is the recommended installation method; the server runs on Python with dependencies defined in the Dockerfile.
@MaartenSmeets
Basic information
MCP server to use a search engine to obtain locations of relevant information on the internet
Add this server to your MCP-compatible client using the configuration below.
{
"mcpServers": {
"mcp-server-search": {
"command": "docker",
"args": [
"build",
"-t",
"mcp-server-search",
"."
]
}
}
}Search Google and return results. Results are presented in a formatted Markdown structure with titles, URLs, and descriptions (when enabled).
MCP Server Search is an MCP (Model Context Protocol) server that provides Google search functionality for AI models. It allows models to search for up-to-date information from the web, with caching, configurable delays, and retry mechanisms to avoid rate limiting.
Install via Docker: build the image, create ./logs and ./cache directories, and run the container with SSE enabled on port 8000. Configure Cline to connect to http://localhost:8000/sse. Use the google_search tool or prompt with a query and optional parameters like num_results (1–20), use_cache, and include_descriptions.
Docker is the recommended installation method; the server runs on Python with dependencies defined in the Dockerfile.
Results are cached in a file-based database at `cache/google_cache.db` (configurable via `--cache-path`), with thread-safe access.
The server uses HTTP SSE (Server-Sent Events) on port 8000 at the `/sse` endpoint.
Yes, via command-line arguments: `--request-delay` (default 5 seconds) and `--max-retries` (default 3).
Yes, the server implements automatic request throttling, random user agent rotation, and exponential backoff on HTTP 429 responses.

Hosted Google Search / SERP MCP server from HasData, no Google Cloud project. Organic results, AI Overview, People Also Ask, AI Mode, news and shopping for Claude, Cursor and any MCP client.
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