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Gemini Embedding 2 MCP Server

@AlaeddineMessadi

代码仓库

基本信息

分类

AI 与智能体

传输方式

stdio

作者

AlaeddineMessadi

提交者

Alaeddine Messadi

关于 Gemini Embedding 2 MCP Server

A powerful Model Context Protocol (MCP) server using gemini embedding 3 that transforms any local directory into an ultrafast, visually-aware spatial search engine for AI agents.

#mcp#context#embeddings#gemini#index#embedding#claude#rag#vector-database#ai-coding#mcp-server#embedding2#gemini-embedding-2#gemini-embedding-2-mcp#gemini-embedding2

Gemini Embedding 2 MCP Server 配置

使用下面的配置,将此服务器添加到你的 MCP 客户端。

{
  "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"
      }
    }
  }
}

Gemini Embedding 2 MCP Server 工具

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点击上方按钮,在临时沙箱中运行此服务器并实时获取工具列表。

概览

What is Gemini Embedding 2 Mcp?

Gemini Embedding 2 Mcp connects local documents, code, images, and videos to Claude, Cursor, or VS Code using Google's gemini-embedding-2-preview model and a strictly local ChromaDB vector database. It is designed for users who need local-first, enterprise-grade semantic search and RAG with full privacy.

How to use Gemini Embedding 2 Mcp?

Install uv (pip install uv), get a free Gemini API key from Google AI Studio, then configure your client using the uvx command with the GEMINI_API_KEY environment variable. Zero-Install (recommended) runs directly from GitHub without cloning; alternatively, clone the repository and run uv sync for local development.

Key features of Gemini Embedding 2 Mcp

  • Local privacy with ChromaDB stored at ~/.gemini_mcp_db
  • Enterprise‑grade embeddings via gemini-embedding-2-preview with MRL 768 optimization
  • Native support for images, video, and audio without text extraction
  • Visual PDF RAG page‑by‑page preserving charts and layout
  • Agentic guardrails: junk filter, wildcard blacklisting, exponential backoff, ghost pruning

Use cases of Gemini Embedding 2 Mcp

  • Index a local codebase and perform semantic searches across source files
  • Run visual RAG on scanned PDFs containing charts and embedded plots
  • Search over images, videos, and audio files without manual transcription
  • Let an autonomous AI agent safely index and sync directories with automatic blacklisting

常见问题

1

Where does my data and embeddings live?

All vectors and metadata are stored locally in ChromaDB under `~/.gemini_mcp_db`. Only raw byte chunks are sent to the Gemini Embedding API; your files never reach a third‑party database.

2

What model and dimensionality are used?

It uses the `gemini-embedding-2-preview` model with the `RETRIEVAL_DOCUMENT` task type and MRL dimensionality of 768.

3

What file types are supported?

Images (`.jpg`, `.webp`), video (`.mp4`), audio (`.mp3`, `.wav`), PDFs (visual RAG), and any text/code files present in indexed directories.

4

How do I install it without cloning the repo?

Use the zero‑install method via `uvx`. Your client (Claude Code, Claude Desktop, Cursor, etc.) points to `uvx --from git+https://github.com/AlaeddineMessadi/gemini-embedding-2-mcp-server.git gemini-embedding-2-mcp` with the `GEMINI_API_KEY` environment variable.

5

What MCP tools are exposed?

`index_directory`, `search_my_documents`, `list_indexed_directories`, `sync_indexed_directories`, `remove_directory_from_index`, and the resource `gemini://database-stats` for real‑time ChromaDB observability.

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