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Provides image generation capabilities using Google's Gemini AI models with customizable parameters like style and temperature

Gemini MCP Server with Smart Tool Intelligence

Welcome to the Gemini MCP Server, the first MCP server with Smart Tool Intelligence - a revolutionary self-learning system that adapts to your preferences and improves over time. This comprehensive platform provides 7 AI-powered tools with automatic prompt enhancement and context awareness.

🚀 Features Overview

🤖 7 AI-Powered Tools

Image Generation - Create images from text prompts using Gemini 2.0 Flash
Image Editing - Edit existing images with natural language instructions
Chat - Interactive conversations with context-aware responses
Audio Transcription - Convert audio to text with optional verbatim mode
Code Execution - Run Python code in a secure sandbox environment
Video Analysis - Analyze video content for summaries, transcripts, and insights
Image Analysis - Extract objects, text, and detailed descriptions from images

🧠 Smart Tool Intelligence System (First in MCP Ecosystem)

Self-Learning - Automatically learns from successful interactions
Context Detection - Recognizes consciousness research, coding, debugging contexts
Pattern Recognition - Identifies usage patterns and user preferences
Prompt Enhancement - Refines prompts for better AI model performance
Persistent Memory - Stores learned preferences across sessions
Automatic Migration - Seamlessly upgrades preference storage

Related MCP server: Gemini MCP Server

📦 Quick Start

Installation

git clone https://github.com/Garblesnarff/gemini-mcp-server.git cd gemini-mcp-server npm install

Configuration

Get your Gemini API key from Google AI Studio
Copy the environment template:
cp .env.example .env
Edit .env and add your API key:
GEMINI_API_KEY=your_actual_api_key_here OUTPUT_DIR=/path/to/your/output/directory # Optional DEBUG=false # Optional

Running the Server

npm start # or for development with debug logging: npm run dev

Integration with Claude Desktop

Add to your Claude Desktop config (claude_desktop_config.json):

{ \"mcpServers\": { \"gemini\": { \"command\": \"node\", \"args\": [\"/path/to/gemini-mcp-server/gemini-server.js\"], \"env\": { \"GEMINI_API_KEY\": \"your_api_key_here\" } } } }

🛠️ Tool Reference

1. Image Generation (`generate_image`)

Generate images from text descriptions using Gemini 2.0 Flash.

Parameters:

prompt (string, required) - Description of the image to generate
context (string, optional) - Context for Smart Tool Intelligence enhancement

Example:

{ \"prompt\": \"A serene mountain landscape at sunset with vibrant colors\", \"context\": \"artistic\" }

Returns:

{ \"content\": [{ \"type\": \"text\", \"text\": \"Generated a beautiful mountain landscape image.\" }, { \"type\": \"image\", \"data\": \"base64_image_data\", \"mimeType\": \"image/png\" }] }

2. Image Editing (`gemini-edit-image`)

Edit existing images using natural language instructions.

Parameters:

image_path (string, required) - Path to the image file to edit
edit_instruction (string, required) - Description of desired changes
context (string, optional) - Context for enhancement

Example:

{ \"image_path\": \"/path/to/image.jpg\", \"edit_instruction\": \"Add shooting stars to the night sky\", \"context\": \"artistic\" }

3. Chat (`gemini-chat`)

Interactive conversations with Gemini AI that learns your preferences.

Parameters:

message (string, required) - Your message or question
context (string, optional) - Context for Smart Tool Intelligence

Example:

{ \"message\": \"Explain quantum computing in simple terms\", \"context\": \"consciousness\" // Will apply academic rigor enhancement }

4. Audio Transcription (`gemini-transcribe-audio`)

Convert audio files to text with Smart Tool Intelligence enhancement.

Parameters:

file_path (string, required) - Path to audio file (MP3, WAV, FLAC, AAC, OGG, WEBM, M4A)
language (string, optional) - Language hint for better accuracy
context (string, optional) - Use "verbatim" for exact word-for-word transcription
preserve_spelled_acronyms (boolean, optional) - Keep U-R-L instead of URL

Example (Standard):

{ \"file_path\": \"/path/to/audio.mp3\", \"language\": \"en\" }

Example (Verbatim Mode):

{ \"file_path\": \"/path/to/audio.mp3\", \"context\": \"verbatim\", // Gets exact word-for-word transcription \"preserve_spelled_acronyms\": true }

Verbatim Mode Features:

Captures all "um", "uh", "like", repeated words
Preserves emotional expressions: [laughs], [sighs], [clears throat]
Maintains original punctuation and sentence structure
No summarization or cleanup

5. Code Execution (`gemini-code-execute`)

Execute Python code in a secure sandbox environment.

Parameters:

code (string, required) - Python code to execute
context (string, optional) - Context for enhancement

Example:

{ \"code\": \"import pandas as pd\\ndata = {'x': [1,2,3], 'y': [4,5,6]}\\ndf = pd.DataFrame(data)\\nprint(df.describe())\", \"context\": \"code\" }

6. Video Analysis (`gemini-analyze-video`)

Analyze video content for summaries, transcripts, and detailed insights.

Parameters:

file_path (string, required) - Path to video file (MP4, MOV, AVI, WEBM, MKV, FLV)
analysis_type (string, optional) - "summary", "transcript", "objects", "detailed", "custom"
context (string, optional) - Context for enhancement

Example:

{ \"file_path\": \"/path/to/video.mp4\", \"analysis_type\": \"detailed\" }

7. Image Analysis (`gemini-analyze-image`)

Extract detailed information from images including objects, text, and descriptions.

Parameters:

file_path (string, required) - Path to image file (JPEG, PNG, WebP, HEIC, HEIF, BMP, GIF)
analysis_type (string, optional) - "summary", "objects", "text", "detailed", "custom"
context (string, optional) - Context for enhancement

Example:

{ \"file_path\": \"/path/to/image.jpg\", \"analysis_type\": \"objects\" }

🧠 Smart Tool Intelligence System

How It Works

The Smart Tool Intelligence system is the first of its kind in the MCP ecosystem. It automatically:

Detects Context - Recognizes if you're doing consciousness research, coding, debugging, etc.
Enhances Prompts - Adds relevant instructions based on learned patterns
Learns Patterns - Stores successful interaction patterns for future use
Adapts Over Time - Gets better at helping you with each interaction

Context Types

The system recognizes these contexts and applies appropriate enhancements:

consciousness - Adds academic rigor, citations, detailed explanations
code - Includes practical examples, working code, best practices
debugging - Focuses on root cause analysis and specific fixes
general - Applies comprehensive, structured responses
verbatim - For audio transcription, provides exact word-for-word output

Storage Location

Preferences are stored internally at ./data/tool-preferences.json with automatic migration from external storage.

Implementing Smart Tool Intelligence in Your MCP Server

Want to add this revolutionary capability to your own MCP server? Here's how:

1. Core Architecture

// src/intelligence/context-detector.js class ContextDetector { detectContext(prompt, toolName) { // Implement pattern matching for different contexts if (this.isConsciousnessContext(prompt)) return 'consciousness'; if (this.isCodeContext(prompt)) return 'code'; if (this.isDebuggingContext(prompt)) return 'debugging'; return 'general'; } } // src/intelligence/prompt-enhancer.js class PromptEnhancer { enhancePrompt(originalPrompt, context, toolName) { // Apply context-specific enhancements const enhancement = this.getEnhancementForContext(context); return `${originalPrompt}\\n\\n${enhancement}`; } } // src/intelligence/preference-store.js class PreferencesManager { async storePattern(original, enhanced, context, toolName, success) { // Store successful patterns for future learning } async getPatterns(context) { // Retrieve learned patterns for context } }

2. Integration Pattern

// In your tool's execute method: async execute(args) { const intelligence = IntelligenceSystem.getInstance(); // Detect context and enhance prompt const context = args.context || intelligence.contextDetector.detectContext(args.prompt, this.name); const enhancedPrompt = await intelligence.enhancePrompt(args.prompt, context, this.name); // Execute with enhanced prompt const result = await this.geminiService.generateContent(enhancedPrompt); // Store successful pattern await intelligence.storeSuccessfulPattern(args.prompt, enhancedPrompt, context, this.name); return result; }

3. Key Implementation Files

Study these files from this repository:

src/intelligence/index.js - Main intelligence coordinator
src/intelligence/context-detector.js - Context recognition logic
src/intelligence/prompt-enhancer.js - Enhancement application
src/intelligence/preference-store.js - Pattern storage and retrieval
src/tools/base-tool.js - Integration with tool execution

🧪 Testing

Run Test Suite

# Test basic functionality npm test # Test Smart Tool Intelligence node test-tool-intelligence-full.js # Test internal storage node test-internal-storage.js # Test verbatim transcription node test-verbatim-mode.js

Manual Testing Examples

# Test image generation echo '{\"jsonrpc\":\"2.0\",\"id\":1,\"method\":\"tools/call\",\"params\":{\"name\":\"generate_image\",\"arguments\":{\"prompt\":\"A cute robot reading a book\"}}}' | node gemini-server.js # Test chat with consciousness context echo '{\"jsonrpc\":\"2.0\",\"id\":2,\"method\":\"tools/call\",\"params\":{\"name\":\"gemini-chat\",\"arguments\":{\"message\":\"What is consciousness?\",\"context\":\"consciousness\"}}}' | node gemini-server.js

📊 Performance & Limits

File Size Limits

Images: 20MB (JPEG, PNG, WebP, HEIC, HEIF, BMP, GIF)
Audio: 20MB (MP3, WAV, FLAC, AAC, OGG, WEBM, M4A)
Video: 100MB (MP4, MOV, AVI, WEBM, MKV, FLV)

API Rate Limits

Follows Google Gemini API rate limits
Built-in error handling and retry logic
Graceful degradation on quota exceeded

🏗️ Architecture Deep Dive

Modular Design

src/ ├── server.js # MCP protocol handler ├── config.js # Configuration management ├── tools/ # Tool implementations │ ├── index.js # Tool registry & dispatcher │ ├── base-tool.js # Abstract base class │ ├── chat.js # Chat tool │ ├── image-generation.js # Image generation tool │ ├── image-editing.js # Image editing tool │ ├── audio-transcription.js # Audio transcription tool │ ├── code-execution.js # Code execution tool │ ├── video-analysis.js # Video analysis tool │ └── image-analysis.js # Image analysis tool ├── intelligence/ # Smart Tool Intelligence │ ├── index.js # Intelligence coordinator │ ├── context-detector.js # Context recognition │ ├── prompt-enhancer.js # Prompt enhancement │ └── preference-store.js # Pattern storage ├── gemini/ # Gemini API integration │ ├── gemini-service.js # API service layer │ └── request-handler.js # Request formatting └── utils/ # Utilities ├── logger.js # Logging system └── file-utils.js # File operations

Intelligence System Flow

Request Received → Tool's execute method called
Context Detection → Analyze prompt for context clues
Pattern Retrieval → Get relevant learned patterns
Prompt Enhancement → Apply context-specific improvements
API Execution → Send enhanced prompt to Gemini
Pattern Storage → Store successful interaction pattern
Response Return → Return enhanced result to user

🔧 Customization

Adding New Contexts

// In src/intelligence/context-detector.js isMyCustomContext(prompt) { const patterns = [ /custom pattern 1/i, /custom pattern 2/i ]; return patterns.some(pattern => pattern.test(prompt)); } // In src/intelligence/prompt-enhancer.js getEnhancementForContext(context) { const enhancements = { 'my_custom_context': 'Apply my custom enhancement instructions here.', // ... other contexts }; return enhancements[context] || enhancements.general; }

Adding New Tools

Create tool file in src/tools/my-new-tool.js
Extend BaseTool class
Implement execute method with intelligence integration
Register in src/tools/index.js

// src/tools/my-new-tool.js class MyNewTool extends BaseTool { constructor(geminiService, intelligenceSystem) { super('my-new-tool', 'Description of my tool', geminiService, intelligenceSystem); } async execute(args) { // Use intelligence system for enhancement const context = args.context || this.detectContext(args.input); const enhancedPrompt = await this.enhancePrompt(args.input, context); // Your tool logic here const result = await this.geminiService.someMethod(enhancedPrompt); // Store successful pattern await this.storeSuccessfulPattern(args.input, enhancedPrompt, context); return result; } }

🐛 Troubleshooting

Common Issues

"Missing GEMINI_API_KEY" Error

# Ensure .env file exists and contains your API key cp .env.example .env # Edit .env and add: GEMINI_API_KEY=your_key_here

"File not found" Errors

# Ensure file paths are absolute and files exist # Check file permissions and formats

Intelligence System Not Learning

# Check data directory permissions ls -la data/ # Verify tool-preferences.json is writable

Debug Mode

DEBUG=true npm start # or npm run dev

Logs Location

Application logs: Console output
Intelligence patterns: ./data/tool-preferences.json
Generated images: $OUTPUT_DIR (default: ~/Claude/gemini-images)

🤝 Contributing

We welcome contributions! This project represents a new paradigm in MCP server development.

Development Setup

git clone https://github.com/Garblesnarff/gemini-mcp-server.git cd gemini-mcp-server npm install npm run dev

Areas for Contribution

New Contexts - Add support for specialized domains
Enhanced Patterns - Improve learning algorithms
New Tools - Expand Gemini AI capabilities
Performance - Optimize intelligence system performance
Documentation - Improve guides and examples

📈 Roadmap

Multi-language Support - Context detection in multiple languages
Advanced Analytics - Usage patterns and performance metrics
Tool Chaining - Intelligent coordination between multiple tools
Custom Models - Support for fine-tuned Gemini models
Collaborative Learning - Share anonymized patterns across instances
Visual Interface - Web-based configuration and monitoring

🌟 Why This Matters

This is the first MCP server that truly learns and adapts. Traditional MCP servers are static - they do the same thing every time. Our Smart Tool Intelligence system represents a paradigm shift toward AI tools that become more helpful over time.

For Users: Better results with less effort as the system learns your preferences.
For Developers: A blueprint for building truly intelligent, adaptive AI tools.
For the MCP Ecosystem: A new standard for what MCP servers can become.

📄 License

This project is licensed under the MIT License - feel free to use, modify, and distribute.

🙏 Acknowledgments

Built with:

Google Gemini AI - Powering the core AI capabilities
Model Context Protocol - Enabling seamless integration
Node.js & NPM - Runtime and package management
Claude & Rob - Human-AI collaboration at its finest

Ready to experience the future of MCP servers? Get started now and watch your AI tools become smarter with every interaction! 🚀"

Gemini MCP Server with Smart Tool Intelligence

🚀 Features Overview

🤖 7 AI-Powered Tools

🧠 Smart Tool Intelligence System (First in MCP Ecosystem)

📦 Quick Start

Installation

Configuration

Running the Server

Integration with Claude Desktop

🛠️ Tool Reference

1. Image Generation (generate_image)

2. Image Editing (gemini-edit-image)

3. Chat (gemini-chat)

4. Audio Transcription (gemini-transcribe-audio)

5. Code Execution (gemini-code-execute)

6. Video Analysis (gemini-analyze-video)

7. Image Analysis (gemini-analyze-image)

🧠 Smart Tool Intelligence System

How It Works

Context Types

Storage Location

Implementing Smart Tool Intelligence in Your MCP Server

1. Core Architecture

2. Integration Pattern

3. Key Implementation Files

🧪 Testing

Run Test Suite

Manual Testing Examples

📊 Performance & Limits

File Size Limits

API Rate Limits

🏗️ Architecture Deep Dive

Modular Design

Intelligence System Flow

🔧 Customization

Adding New Contexts

Adding New Tools

🐛 Troubleshooting

Common Issues

Debug Mode

Logs Location

🤝 Contributing

Development Setup

Areas for Contribution

📈 Roadmap

🌟 Why This Matters

📄 License

🙏 Acknowledgments

Resources

Tools

Appeared in Searches

New MCP Servers

Latest Blog Posts

MCP directory API

1. Image Generation (`generate_image`)

2. Image Editing (`gemini-edit-image`)

3. Chat (`gemini-chat`)

4. Audio Transcription (`gemini-transcribe-audio`)

5. Code Execution (`gemini-code-execute`)

6. Video Analysis (`gemini-analyze-video`)

7. Image Analysis (`gemini-analyze-image`)