Enhanced Gemini MCP Server

Intelligent Codebase Analysis with Gemini's 1M Token Context Window

Transform your development workflow with this enhanced MCP server that leverages Google's Gemini AI for comprehensive codebase analysis. Load entire projects into Gemini's massive context window and get intelligent insights about your code.

Key Features

Intelligent Codebase Loading - Smart file prioritization and relevance scoring
1M Token Context - Leverage Gemini's full context window for comprehensive analysis
Semantic Search - Natural language code search across your entire project
Architecture Analysis - Get detailed insights into your codebase structure
Smart Improvements - Targeted suggestions for performance, security, and maintainability
Code Flow Tracing - Understand how functionality works across files
Project Summaries - Comprehensive overviews of any codebase
Session Caching - 30-minute cache for faster repeated analyses
Export Sessions - Save important findings for future reference

Quick Start

Prerequisites

Python 3.8+ installed
Claude Code CLI installed
Google Gemini API key (Get one free)

Installation

Clone the repository:

git clone https://github.com/ECamposSoria/gemini-mcp-enhanced.git
cd gemini-mcp-enhanced

Run enhanced setup:

./enhanced_setup.sh

Enter your Gemini API key when prompted

That's it!

Available Tools

Core Analysis Tools

`load_codebase`

Load an entire project into Gemini's context with intelligent prioritization:

mcp__gemini-collab-enhanced__load_codebase
  project_path: "/path/to/your/project"
  max_tokens: 900000  # optional, default: 900000

`analyze_architecture`

Get comprehensive architecture analysis:

mcp__gemini-collab-enhanced__analyze_architecture
  focus: "architecture"  # or "patterns", "dependencies", "structure"

`semantic_search`

Search your codebase using natural language:

mcp__gemini-collab-enhanced__semantic_search
  query: "Find functions that handle user authentication"

`suggest_improvements`

Get targeted improvement suggestions:

mcp__gemini-collab-enhanced__suggest_improvements
  area: "security"  # or "performance", "maintainability", "testing"

`explain_codeflow`

Trace how functionality works across your codebase:

mcp__gemini-collab-enhanced__explain_codeflow
  functionality: "How user registration works from API to database"

`codebase_summary`

Get a comprehensive project overview:

mcp__gemini-collab-enhanced__codebase_summary

`ask_with_context`

Ask any question about your loaded codebase:

mcp__gemini-collab-enhanced__ask_with_context
  question: "What security vulnerabilities exist in the authentication system?"

Session Management Tools

`export_session`

Export cached analysis results to a markdown file:

mcp__gemini-collab-enhanced__export_session
  export_path: "/path/to/export"  # optional, defaults to project path

`session_stats`

Get current session cache statistics:

mcp__gemini-collab-enhanced__session_stats

Session Caching

The enhanced server includes intelligent session caching to improve performance:

Automatic Caching: Analysis results are cached for 30 minutes
Smart Invalidation: Cache clears when switching projects
Performance Boost: Repeated queries return instantly from cache
Export Capability: Save important findings before they expire

Caching Benefits

Faster Iterations: Re-run analyses without waiting
Token Savings: Avoid redundant API calls to Gemini
Persistent Insights: Export sessions for long-term reference

Usage Examples

Complete Workflow Example

# Start Claude Code
claude

# Load your project
mcp__gemini-collab-enhanced__load_codebase
  project_path: "/home/user/my-app"

# Get architecture overview
mcp__gemini-collab-enhanced__analyze_architecture

# Find specific functionality
mcp__gemini-collab-enhanced__semantic_search
  query: "payment processing logic"

# Get security improvements
mcp__gemini-collab-enhanced__suggest_improvements
  area: "security"

# Ask specific questions
mcp__gemini-collab-enhanced__ask_with_context
  question: "How can I optimize the database queries in the user service?"

How It Works

Intelligent File Prioritization

The server uses a sophisticated scoring system to determine which files are most relevant:

Language weights - Core languages (Python, JavaScript, TypeScript) get higher priority
Directory importance - src/, lib/, core/ directories are prioritized
File size optimization - Balances completeness with context limits
Special files - main.py, index.js, app.py get bonus priority

Smart Token Management

Uses tiktoken for accurate token counting
Stays within Gemini's 1M token limit intelligently
Fallback estimation when tiktoken unavailable
Structured context formatting for optimal analysis

Context Creation

Creates comprehensive context including:

File tree structure with metadata
Complete file contents with syntax highlighting
Relevance scores and language detection
Project statistics and organization

Technical Details

Supported File Types

Languages: Python, JavaScript, TypeScript, Java, C++, Go, Rust, Swift, Kotlin, Scala
Web: HTML, CSS, SCSS, Vue, Svelte, React (JSX/TSX)
Config: JSON, YAML, XML, Dockerfile, Terraform
Documentation: Markdown, SQL scripts

Performance Features

Intelligent file filtering (skips node_modules, .git, __pycache__, etc.)
Relevance-based file selection
Token-aware content loading
Efficient context creation

Installation Details

The enhanced server is installed at: ~/.claude-mcp-servers/gemini-collab-enhanced/

File Structure

~/.claude-mcp-servers/gemini-collab-enhanced/
├── enhanced_server.py      # Main server with intelligent analysis
├── requirements.txt        # Python dependencies
└── test_enhanced.py       # Test suite

Troubleshooting

Server not showing up?

# Check installation
claude mcp list

# Reinstall if needed
claude mcp remove gemini-collab-enhanced
./enhanced_setup.sh

Token counting issues?

# Install tiktoken for accurate counting
pip install tiktoken>=0.5.1

Large codebase not loading completely?

Increase max_tokens parameter
Check file relevance scores
Exclude unnecessary directories

Connection errors?

Verify Gemini API key is valid
Check internet connection
Ensure dependencies are installed: pip install -r requirements.txt

Best Practices

For Large Codebases

Start with architectural analysis
Use semantic search to find specific areas
Focus improvement suggestions on specific areas
Break down complex questions into smaller parts

For Security Analysis

Load codebase
Run security-focused improvements
Use semantic search for auth/validation code
Ask specific security questions with context

For Performance Optimization

Analyze architecture first
Search for performance-critical code
Get performance-focused suggestions
Trace code flow for bottlenecks

Contributing

Contributions welcome! Please:

Fork the repository
Create a feature branch
Add tests for new functionality
Submit a pull request

License

MIT License - Use freely!

Acknowledgments

Built for the Claude Code community
Powered by Google's Gemini AI
Enhanced by ECamposSoria

Ready to revolutionize your code analysis?

Start by loading your first codebase and experience the power of AI-driven development insights!