claude-gemini-mcp-slim

🥈Silver

Lightweight integration of Google's Gemini AI with Claude Code, offering 1M+ token context window, smart model selection, and code analysis tools for operations teams. Connects to existing workflows via Python, enhancing automation and code understanding.

204170Updated Today

Intermediate30min to implementautomation

Saves ~300 min per use

Quick InstallView Source

git clone https://github.com/cmdaltctr/claude-gemini-mcp-slim.git

Works with:

Claude

Overview

About This Skill

Setup & Installation

Quick Install

No install command available. Check the GitHub repository for manual installation instructions.

Alternative Install (Git Clone)

git clone https://github.com/cmdaltctr/claude-gemini-mcp-slim

Requirements

Claude Code or compatible AI agent
Works with: Claude

Quick Start Guide

Install the Skill

Copy the install command above and run it in your terminal.

Open Your AI Agent

Launch Claude Code, Cursor, or your preferred AI coding agent.

Try It Out

Use the prompt template or examples below to test the skill.

Customize

Adapt the skill to your specific use case and workflow.

Usage Examples

Prompt Template

Analyze the following [CODE] from [COMPANY] in the [INDUSTRY] sector. Identify potential optimizations, security vulnerabilities, and compatibility issues with Gemini AI integration. Provide a detailed report with actionable recommendations.

Example Output

# Code Analysis Report

## Overview
The provided codebase from TechSolutions Inc. in the fintech industry has been analyzed for optimization, security, and compatibility with Gemini AI integration.

## Key Findings
- **Optimization Opportunities**:
  - Redundant data processing in `data_handler.py` (Lines 45-78)
  - Inefficient database queries in `query_module.py` (Lines 112-145)
- **Security Vulnerabilities**:
  - SQL injection risk in `user_auth.py` (Lines 22-35)
  - Insufficient input validation in `api_gateway.py` (Lines 88-102)
- **Compatibility Issues**:
  - Potential conflicts with Gemini's natural language processing modules
  - Incompatible data structures for seamless integration

## Recommendations
1. Refactor `data_handler.py` to eliminate redundant processing
2. Implement prepared statements in `user_auth.py` to prevent SQL injection
3. Update `api_gateway.py` with comprehensive input validation
4. Modify data structures to align with Gemini's requirements
5. Conduct thorough testing after implementing changes