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brave-mcp-search

MCP.Pizza Chef: arben-adm

Brave-mcp-search is a Model Context Protocol (MCP) server that connects Brave Search with AI assistants such as Claude, Gemini, and OpenAI models. It enables real-time search context integration, allowing LLMs to query Brave Search results directly within their workflows. This server supports Python 3.11+ and can be installed via Smithery or manually, facilitating seamless AI-enhanced search experiences in various applications.

Use This MCP server To

Integrate Brave Search results into AI assistant workflows Enable real-time web search context for LLMs Provide search-based data for multi-step reasoning Enhance chatbots with up-to-date search information Automate retrieval of web data during conversations Combine Brave Search with Claude or Gemini for research tasks

README

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.

brave-mcp-search FAQ

How do I install the brave-mcp-search server?
You can install it automatically via Smithery CLI or manually by cloning the repo and setting up a Python 3.11+ environment.
Which AI assistants are compatible with brave-mcp-search?
It supports integration with Claude, OpenAI GPT models, Gemini, and other MCP-compatible LLMs.
What are the prerequisites for running brave-mcp-search?
You need Python 3.11 or higher and the 'uv' package installer for dependency management.
Can brave-mcp-search be used with multiple LLM providers?
Yes, it is provider-agnostic and works with Claude, OpenAI, Gemini, and others.
Is brave-mcp-search suitable for real-time search queries?
Yes, it enables real-time search context feeding into LLMs for dynamic query responses.
How does brave-mcp-search improve AI assistant capabilities?
By providing direct access to Brave Search results, it enriches AI responses with current web data.
What platforms support brave-mcp-search installation?
It supports any platform that can run Python 3.11+, including Windows, macOS, and Linux.
How do I update brave-mcp-search dependencies?
Use the 'uv' package manager within the virtual environment to update dependencies efficiently.