Gemini Embedding 2 Mcp

Name: Gemini Embedding 2 Mcp
Author: AlaeddineMessadi

@AlaeddineMessadi

关于 Gemini Embedding 2 Mcp

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

基本信息

分类

AI 与智能体

传输方式

stdio

发布者

AlaeddineMessadi

提交者

Alaeddine Messadi

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"
      }
    }
  }
}

工具

未检测到工具

工具是从 README 中自动提取的。维护者可以在 ## Tools 标题下列出工具,即可填充这部分内容。

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

FAQ from Gemini Embedding 2 Mcp

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.

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.

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.

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.

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.