Gemini Embedding 2 Mcp

Connect your local documents, code, images, and videos directly to Claude, Cursor, or VS Code using Google's state-of-the-art gemini-embedding-2-preview model and a strictly local ChromaDB vector database.

✨ Key Features

🚀 Installation & Setup

We support two ways to run this server: Zero-Install (Recommended) or Local Developer Clone. Make sure you have uv installed on your machine (pip install uv).

Method 1: Zero-Install (Recommended)

You can point your AI assistant to run the server directly from GitHub without ever cloning the repository locally. uvx acts like npx for Python, downloading and caching the server in a secure ephemeral environment automatically!

🔑 Getting your Gemini API Key

To power the embedding model, you need a free API key from Google.

1. Go to Google AI Studio.
2. Click Create API key.
3. Copy the key and use it in your client configurations below as GEMINI_API_KEY.

🔌 Client Connection Guides

🤖 Claude Code (CLI)

You can attach this server to the Claude Code CLI natively. Run the following command in your terminal:

claude mcp add gemini-embedding-2-mcp \
  --env GEMINI_API_KEY="your-api-key-here" \
  -- uvx --from git+https://github.com/AlaeddineMessadi/gemini-embedding-2-mcp-server.git gemini-embedding-2-mcp

🦋 Claude Desktop

Open your Claude Desktop config file (usually ~/Library/Application Support/Claude/claude_desktop_config.json on macOS) and add:

{
  "mcpServers": {
    "gemini-embedding-2-mcp": {
      "command": "uvx",
      "args": [
        "--from",
        "git+https://github.com/AlaeddineMessadi/gemini-embedding-2-mcp-server.git",
        "gemini-embedding-2-mcp"
      ],
      "env": {
        "GEMINI_API_KEY": "your-api-key-here"
      }
    }
  }
}

💻 Cursor IDE

1. Go to Settings > Features > MCP
2. Click + Add new MCP server
3. Choose command as the type.
4. Name: gemini-embedding
5. Command: GEMINI_API_KEY="your-api-key" uvx --from git+https://github.com/AlaeddineMessadi/gemini-embedding-2-mcp-server.git gemini-embedding-2-mcp

🏄‍♂️ Windsurf (Cascade)

Open your ~/.codeium/windsurf/mcp_config.json file and add:

{
  "mcpServers": {
    "gemini-embedding-2-mcp": {
      "command": "uvx",
      "args": [
        "--from",
        "git+https://github.com/AlaeddineMessadi/gemini-embedding-2-mcp-server.git",
        "gemini-embedding-2-mcp"
      ],
      "env": {
        "GEMINI_API_KEY": "your-api-key-here"
      }
    }
  }
}

⚡ Zed Editor

Open your ~/.config/zed/settings.json and append the MCP server block:

{
  "experimental.mcp": {
    "gemini-embedding-2-mcp": {
      "command": "uvx",
      "args": [
        "--from",
        "git+https://github.com/AlaeddineMessadi/gemini-embedding-2-mcp-server.git",
        "gemini-embedding-2-mcp"
      ],
      "env": {
        "GEMINI_API_KEY": "your-api-key-here"
      }
    }
  }
}

💻 VS Code (with Cline / RooCode)

Open ~/Library/Application Support/Code/User/globalStorage/saoudrizwan.claude-dev/settings/cline_mcp_settings.json and append:

{
  "mcpServers": {
    "gemini-embedding": {
      "command": "uvx",
      "args": [
        "--from",
        "git+https://github.com/AlaeddineMessadi/gemini-embedding-2-mcp-server.git",
        "gemini-embedding-2-mcp"
      ],
      "env": {
        "GEMINI_API_KEY": "your-api-key-here"
      }
    }
  }
}

Method 2: Local Developer Clone

If you want to modify the source code:

# 1. Clone the repository
git clone https://github.com/AlaeddineMessadi/gemini-embedding-2-mcp-server.git
cd gemini-embedding-2-mcp-server

# 2. Install dependencies
uv sync

(If you use this method, you can add it directly to Claude Code CLI locally by running:)

claude mcp add gemini-embedding-local --env GEMINI_API_KEY="your-api-key" -- uv --directory "$(pwd)" run gemini-embedding-2-mcp

🛠️ Exposed MCP Capabilities

Once connected, your AI assistant instantly gains the following tools:

⚙️ Tools

• index_directory(path: str, ignore: list = None): Scan and formally embed a completely new local folder into the DB. Safely supports wildcard ignore patterns.
• search_my_documents(query: str, limit: int): Run lighting-fast semantic cosine-similarity spatial search over the indexed database.
• list_indexed_directories(): See what paths the AI already knows about.
• sync_indexed_directories(): Automatically forces the DB to find new, updated, or recently deleted (ghost) files and cleans up vectors.
• remove_directory_from_index(path: str): Clears a specific trajectory of vectors.

📊 Resources

• gemini://database-stats: Real-time observability! Exposes the exact scale of the vector segments inside ChromaDB directly to the assistant's context.

📚 Technical Documentation

• Architecture Deep Dive
• Ultimate Multimodality & PDF RAG
• Agentic Safety Guardrails

📜 License

MIT © Alaeddine Messadi