Is the gemini-mcp MCP server actively maintained?

The gemini-mcp MCP server was last committed 3mo ago and has 7 contributors. Issue health: 67% of issues resolved. AgentRank freshness score reflects real-time maintenance activity.

gemini-mcp MCP Server

Name: RLabs-Inc/gemini-mcp
Rating: 3.4 (1 reviews)
Author: RLabs-Inc

RLabs-Inc/gemini-mcp

Score: 59.1 ↓ 3.5% Rank #6647 MCP Server

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MCP Server that enables Claude code to interact with Gemini

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Overview

RLabs-Inc/gemini-mcp is a TypeScript MCP server licensed under MIT. MCP Server that enables Claude code to interact with Gemini Topics: claude-code, gemini, mcp, mcp-server.

Ranked #6647 out of 24846 indexed tools.

Ecosystem

TypeScript MIT

claude-codegeminimcpmcp-server

Score Breakdown

Stars 15% 217

217 stars → growing community

Freshness 25% 3mo ago

Last commit 3mo ago → stale

Issue Health 25% 67%

14/21 issues closed → decent issue management

Contributors 10% 7

7 contributors → small team

Dependents 25% 0

No dependents → no downstream usage

npm Downloads N/A

PyPI Downloads N/A

Forks 46

Description Good

License MIT

Weights: Freshness 20% · Issue Health 20% · Dependents 22% · Stars 10% · Contributors 8% · How we score →

How to Improve

Description low impact

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Freshness high impact

Last commit was 113 days ago — a recent commit would boost your freshness score

Dependents medium impact

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

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Details

Stars: 217
Forks: 46
Open Issues: 7
Closed Issues: 14
Contributors: 7
Dependents: —
Language: TypeScript
License: MIT
Last Commit: 3mo ago

Matched Queries

"mcp server""mcp-server"topic:mcptopic:mcp-server

From the README

# MCP Server Gemini

A Model Context Protocol (MCP) server for integrating Google's Gemini 3 models with Claude Code, enabling powerful collaboration between both AI systems. Now with a beautiful CLI!

**MCP Registry Support:** Now discoverable in the official MCP ecosystem!

## Features

| Feature                       | Description                                                     |
|-------------------------------|-----------------------------------------------------------------|
| **Deep Research Agent**       | Autonomous multi-step research with web search and citations    |
| **Token Counting**            | Count tokens and estimate costs before API calls                |
| **Text-to-Speech**            | 30 unique voices, single speaker or two-speaker dialogues       |
| **URL Analysis**              | Analyze, compare, and extract data from web pages               |
| **Context Caching**           | Cache large documents for efficient repeated queries            |
| **YouTub

Read full README on GitHub →