Glama
Gemini Thinking Server

# Model Context Protocol with Gemini Integration - Implementation Summary

## Overview

This implementation creates a Model Context Protocol (MCP) server that integrates with Google's Gemini API to provide analytical thinking capabilities without code generation. The server follows the MCP standard and leverages Gemini's advanced reasoning capabilities to analyze problems, especially in the context of codebases.

## Architecture

The implementation consists of the following components:

1. **GeminiThinkingServer** - Core class that manages communication with Gemini API and processes thoughts
2. **MCP Server** - Standard MCP server implementation that handles tool requests
3. **Tool Definition** - Definition of the "geminithinking" tool with its schema
4. **Client Examples** - Sample clients that demonstrate how to use the server

### Component Details

#### GeminiThinkingServer

This class is responsible for:
- Managing the session with Gemini API
- Validating and processing thought data
- Formatting thoughts for display
- Extracting meta-information from Gemini responses
- Maintaining thought history and branches
- Saving and loading analysis sessions

#### MCP Server

The server implements the Model Context Protocol standard:
- Handles tool listing requests
- Processes tool call requests
- Manages the connection with clients via stdio transport

#### Tool Definition

The "geminithinking" tool is defined with:
- Comprehensive description of capabilities
- Input schema with required and optional parameters
- Support for various thinking patterns (sequential, branching, revision)
- Session management commands

#### Client Examples

Multiple client examples demonstrate:
- Basic usage with simple queries
- Codebase analysis with filtering
- Interactive exploration of alternative paths
- Session persistence and management
- Advanced semantic filtering of codebases

## Key Features

1. **Gemini-Powered Thinking**
   - Leverages Gemini's analytical capabilities
   - Prevents code generation (focuses on analysis only)
   - Configurable generation parameters

2. **Meta-Commentary System**
   - Provides insights into the reasoning process
   - Includes confidence levels for generated thoughts
   - Suggests alternative approaches

3. **Thought Management**
   - Sequential thinking with adjustable total thoughts
   - Branching to explore alternative paths
   - Revision of previous thoughts

4. **Codebase Integration**
   - Support for analyzing code repositories
   - Basic and advanced filtering of relevant parts of the codebase
   - Semantic-based code filtering using keywords and relevance scoring
   - Automatic extraction of keywords from queries
   - Context-aware analysis

5. **Visualization**
   - Formatted output with clear thought boundaries
   - Color-coded thought types (regular, revision, branch)
   - Display of meta-information

6. **Session Persistence**
   - Save analysis sessions to files
   - Load sessions to resume analysis
   - Query current session state
   - Manage multiple analysis sessions

## Implementation Files

1. **gemini-index.ts** - Main server implementation
2. **gemini-package.json** - Package configuration
3. **gemini-tsconfig.json** - TypeScript configuration
4. **README.md** - Documentation and usage instructions
5. **CODEBASE_INTEGRATION.md** - Guide for integrating with codebases
6. **sample-client.js** - Basic client example
7. **codebase-analysis-example.js** - Example for codebase analysis
8. **example-usage.js** - Specific usage example
9. **session-example.js** - Example demonstrating session persistence
10. **advanced-filtering-example.js** - Example demonstrating advanced semantic filtering

## Usage Workflow

1. **Setup**
   - Install dependencies
   - Configure Gemini API key
   - Build the project

2. **Basic Usage**
   - Start the server
   - Connect a client
   - Submit a query with optional context
   - Process thoughts sequentially

3. **Codebase Analysis**
   - Generate a consolidated view of the codebase
   - Apply basic or advanced filtering to identify relevant code
   - Submit analysis queries
   - Explore thoughts and alternative paths

4. **Advanced Features**
   - Branch thoughts to explore alternatives
   - Revise previous thoughts
   - Save analysis results for future reference
   - Save and resume analysis sessions
   - Use semantic filtering to focus on relevant code sections

## Technical Considerations

1. **API Key Management**
   - Secure storage of Gemini API key
   - Environment variable configuration

2. **Error Handling**
   - Graceful handling of API errors
   - Validation of input parameters
   - Fallback mechanisms

3. **Performance**
   - Efficient processing of large codebases
   - Filtering mechanisms to reduce context size
   - Optimized API calls

4. **Extensibility**
   - Modular design for future enhancements
   - Support for additional Gemini models
   - Potential for additional tools

5. **Session Management**
   - JSON-based session storage format
   - Validation of loaded session data
   - Error handling for session operations

6. **Advanced Filtering**
   - Keyword extraction from queries
   - Relevance scoring for files
   - File type and content-based weighting
   - Customizable filtering with additional keywords

## Future Enhancements

1. **Further Filtering Improvements**
   - Machine learning-based relevance prediction
   - Code dependency analysis for more accurate filtering
   - User feedback integration to improve filtering accuracy

2. **Visualization Improvements**
   - Interactive thought maps
   - Relationship visualization between thoughts

3. **Integration with Development Tools**
   - IDE plugins
   - CI/CD pipeline integration

4. **Multi-Model Support**
   - Support for other AI models
   - Model comparison capabilities

5. **Enhanced Session Management**
   - Encrypted session storage
   - Cloud-based session synchronization
   - Session sharing and collaboration

## Conclusion

This implementation provides a powerful integration between the Model Context Protocol and Google's Gemini API, enabling sophisticated analytical thinking capabilities without code generation. It's particularly valuable for understanding and improving codebases through AI-assisted analysis.

The modular architecture ensures extensibility, while the comprehensive documentation and examples make it accessible to users with varying levels of expertise. The addition of session persistence functionality allows users to save their progress and resume analysis sessions later, making it more practical for complex, long-running analyses. The advanced filtering capabilities help users focus on the most relevant parts of large codebases, improving the quality and relevance of the analysis.