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Gemini Embedding 2 Mcp

@AlaeddineMessadi

Repository

About 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

Basic information

Category

AI & Agents

Transports

stdio

Publisher

AlaeddineMessadi

Submitted by

Alaeddine Messadi

Config

Add this server to your MCP-compatible client using the configuration below.

{
  "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"
      }
    }
  }
}

Tools

No tools detected

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Overview

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.

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