Name: AlaeddineMessadi/gemini-embedding-2-mcp-server
Rating: 3.2 (1 reviews)
Author: AlaeddineMessadi

AlaeddineMessadi/gemini-embedding-2-mcp-server

Score: 55.1 Rank #22 Database

🚀 A powerful Model Context Protocol (MCP) server powered by Gemini Embedding 2. Transforms any local directory into an ultrafast, visually-aware spatial search engine for AI agents! 🔎✨

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Overview

AlaeddineMessadi/gemini-embedding-2-mcp-server is a Python MCP server licensed under MIT. 🚀 A powerful Model Context Protocol (MCP) server powered by Gemini Embedding 2. Transforms any local directory into an ultrafast, visually-aware spatial search engine for AI agents! 🔎✨ Topics: context, embedding, embeddings, gemini, index, mcp, vector-database, embedding2, gemini-embedding-2, gemini-embedding-2-mcp, gemini-embedding2, ai-coding, claude, mcp-server, rag.

Ranked #22 out of 110 indexed tools.

Actively maintained with commits in the last week.

Ecosystem

Python MIT

contextembeddingembeddingsgeminiindexmcpvector-databaseembedding2gemini-embedding-2gemini-embedding-2-mcpgemini-embedding2ai-codingclaudemcp-serverrag

Score Breakdown

Stars 15% 1

1 stars → early stage

Freshness 25% today

Last commit today → actively maintained

Issue Health 25% 50%

No issues filed → no history to score

Contributors 10% 1

1 contributor → solo project

Dependents 25% 0

No dependents → no downstream usage

npm Downloads N/A

PyPI Downloads N/A

Forks 0

Description Detailed

License MIT

Weights: Freshness 25% · Issue Health 25% · Dependents 25% · Stars 15% · Contributors 10% · How we score →

How to Improve

Contributors medium impact

Single-contributor projects carry bus-factor risk — welcoming contributors boosts confidence

Dependents medium impact

No downstream dependents detected yet — adoption by other projects is the strongest trust signal

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Markdown

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Details

Stars: 1
Forks: 0
Open Issues: 0
Closed Issues: 0
Contributors: 1
Dependents: —
Language: Python
License: MIT
Last Commit: today

Matched Queries

"mcp server""mcp-server""model context protocol""model-context-protocol"

From the README

<div align="center">
  

  <p align="center">
    <strong>A powerful Model Context Protocol (MCP) server that transforms any local directory into an ultrafast, visually-aware spatial search engine for AI agents.</strong>
  </p>
  
  
  
  
</div>

---

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

| Feature | Description |
| :--- | :--- |
| 🛡️ **Local Privacy** | Uses ChromaDB entirely locally (`~/.gemini_mcp_db`). Your files never go to a 3rd party database. Only raw byte chunks are sent to the Gemini Embedding API. |
| 🧠 **Enterprise-Grade** | Leverages `gemini-embedding-2-preview` with specialized `RETRIEVAL_DOCUMENT` Task Types and MRL `768` dimensionality optimization. |
| 📸 **Ultimate Multimodality** | Natively scans, embeds, and retrieves **Images** (`.jpg`, `.webp`), **Video** (`.

Read full README on GitHub →