arben adm brave mcp search

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What is arben adm brave mcp search

Brave Search MCP Server

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This project implements a Model Context Protocol (MCP) server for Brave Search, allowing integration with AI assistants like Claude.

Prerequisites

  • Python 3.11+
  • uv - A fast Python package installer and resolver

Installation

Installing via Smithery

To install Brave Search MCP server for Claude Desktop automatically via Smithery:

npx -y @smithery/cli install @arben-adm/brave-mcp-search --client claude

Manual Installation

  1. Clone the repository:

    git clone https://github.com/your-username/brave-search-mcp.git
    cd brave-search-mcp
    
  2. Create a virtual environment and install dependencies using uv:

    uv venv
    source .venv/bin/activate  # On Windows, use: .venv\Scripts\activate
    uv pip install -r requirements.txt
    
  3. Set up your Brave Search API key:

    export BRAVE_API_KEY=your_api_key_here
    

    On Windows, use: set BRAVE_API_KEY=your_api_key_here

Usage

  1. Configure your MCP settings file (e.g., claude_desktop_config.json) to include the Brave Search MCP server:

    {
      "mcpServers": {
        "brave-search": {
          "command": "uv",
          "args": [
            "--directory",
            "path-to\\mcp-python\\brave-mcp-search\\src",
            "run",
            "server.py"
          ],
          "env": {
            "BRAVE_API_KEY": "YOUR_BRAVE_API_KEY_HERE"
          }
        }
      }
    }
    

    Replace YOUR_BRAVE_API_KEY_HERE with your actual Brave API key.

  2. Start the Brave Search MCP server by running your MCP-compatible AI assistant with the updated configuration.

  3. The server will now be running and ready to accept requests from MCP clients.

  4. You can now use the Brave Search functionality in your MCP-compatible AI assistant (like Claude) by invoking the available tools.

Available Tools

The server provides two main tools:

  1. brave_web_search: Performs a web search using the Brave Search API.
  2. brave_local_search: Searches for local businesses and places.

Refer to the tool docstrings in src/server.py for detailed usage information.

Development

To make changes to the project:

  1. Modify the code in the src directory as needed.
  2. Update the requirements.txt file if you add or remove dependencies:
    uv pip freeze > requirements.txt
    
  3. Restart the server to apply changes.

Troubleshooting

If you encounter any issues:

  1. Ensure your Brave API key is correctly set.
  2. Check that all dependencies are installed.
  3. Verify that you're using a compatible Python version.
  4. If you make changes to the code, make sure to restart the server.

License

This project is licensed under the MIT License - see the LICENSE file for details.

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Frequently Asked Questions

What is MCP?

MCP (Model Context Protocol) is an open protocol that standardizes how applications provide context to LLMs. Think of MCP like a USB-C port for AI applications, providing a standardized way to connect AI models to different data sources and tools.

What are MCP Servers?

MCP Servers are lightweight programs that expose specific capabilities through the standardized Model Context Protocol. They act as bridges between LLMs like Claude and various data sources or services, allowing secure access to files, databases, APIs, and other resources.

How do MCP Servers work?

MCP Servers follow a client-server architecture where a host application (like Claude Desktop) connects to multiple servers. Each server provides specific functionality through standardized endpoints and protocols, enabling Claude to access data and perform actions through the standardized protocol.

Are MCP Servers secure?

Yes, MCP Servers are designed with security in mind. They run locally with explicit configuration and permissions, require user approval for actions, and include built-in security features to prevent unauthorized access and ensure data privacy.

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