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MCP Server Search

MCP Server Search

Local
github.com/MaartenSmeetsSearch2Updated 72w ago

About MCP Server Search

MCP Server Search exposes Google search to AI models, with caching and request controls. It supports simple and advanced searches and returns formatted results with titles, URLs, and optional descriptions.

Key features

  1. 01Search Google with simple or advanced queries
  2. 02Cache search results
  3. 03Configure request delays and retries
  4. 04Throttle requests and rotate user agents
  5. 05Return formatted results with descriptions

Use cases

  1. 01Finding current information on the web
  2. 02Searching with cached results
  3. 03Reducing rate-limit issues during repeated searches

MCP support

Exposes a `google_search` tool and prompt for Google searches. MCP clients can connect through SSE or use the server's configured transport.

Configuration

Local server config is the same for every client. Copy it into your client’s MCP config file.

mcp.json · Universal config
{
"mcpServers": {
"search": {
"command": "docker",
"args": [
"run",
"-i",
"--rm",
"build"
]
}
}
}
Run commanddocker run -i --rm build

Local servers need a runtime on your machine (e.g. Node.js 18+). Config file locations differ by client; see your client’s docs.

Packages
ocibuild

Tools

No tool list yet

Local servers expose tools only once running on your machine, and the maintainer has not provided a tool list.

Details

From the project README

MCP Server Search

An MCP (Model Context Protocol) server that provides Google search functionality for AI models. This server allows models to search for up-to-date information from the web.

Features

  • Google search integration with caching
  • Configurable request delays and retries to avoid rate limiting
  • Support for both simple and advanced searches (with descriptions)
  • Throttling and randomization to be a good web citizen

Installation

Using Docker (Recommended)
  1. Build the Docker image:

    docker build -t mcp-server-search .
    
  2. Create required directories for persistence:

    mkdir -p ./logs ./cache
    
  3. Run the container (with SSE enabled):

    docker run --rm -p 8000:8000 \
      -v "$(pwd)/logs:/app/logs" \
      -v "$(pwd)/cache:/app/cache" \
      mcp-server-search
    

This will start the server with SSE enabled and make it accessible at http://localhost:8000/sse.

The volumes ensure:

  • Logs are persisted to ./logs directory
  • Search cache is persisted to ./cache directory
  • Both directories are automatically created by Docker if they don't exist

Note: The -i flag is required for MCP protocol communication, and --rm automatically removes the container when it exits.

Cline Integration

To use this MCP server with Cline, add the following configuration to your Cline MCP settings file (located at ~/.config/Code/User/globalStorage/rooveterinaryinc.roo-cline/settings/cline_mcp_settings.json for VS Code):

{
    "mcpServers": {
        "google_search": {
            "url": "http://localhost:8000/sse"
        }
    }
}

This configuration:

  • Connects to the search MCP server via HTTP SSE on port 8000 at the /sse endpoint
  • No longer uses the Docker stdio transport; Cline connects directly to the HTTP endpoint

Configuration

The server accepts the following command-line arguments:

  • --log-level: Set the logging level (DEBUG, INFO, WARNING, ERROR, CRITICAL). Default: INFO
  • --log-file: Path to log file (default: stdout)
  • --cache-path: Path to the search cache file (default: cache/google_cache.db)
  • --request-delay: Delay between search requests in seconds (default: 5)
  • --max-retries: Maximum number of retries for failed searches (default: 3)

Usage

The server exposes the following MCP endpoints:

Tools
  • google_search: Search Google and return results. Results are presented in a formatted Markdown structure with titles, URLs, and descriptions (when enabled).

    Parameters:

    • query (string, required): The search query to execute
    • num_results (integer, optional): Number of results to return (min: 1, max: 20, default: 5)
    • use_cache (boolean, optional): Whether to use cached results if available (default: true)
    • include_descriptions (boolean, optional): Whether to include descriptions in results (default: true)

    Features:

    • Automatic request throttling and retry mechanism
    • Random user agent rotation for better request distribution
    • File-based caching with thread-safe access
    • Exponential backoff on rate limiting (HTTP 429)
Prompts
  • google_search: Search Google with the given query. Returns results in a conversational format.

    Parameters:

    • query (string, required): The search query to execute
    • num_results (integer, optional): Number of results to return (1-20, default: 5)

About MCP

The Model Context Protocol (MCP) is a protocol for connecting Large Language Models (LLMs) with tools and data sources. Learn more at github.com/modelcontextprotocol.

Comments

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New to MCP?

From what the protocol is to installing your first server, these answers cover most first questions.

An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.