my-pal-mcp-server/docs

Gemini MCP Server Documentation

Welcome to the comprehensive documentation for the Gemini MCP Server - a sophisticated Model Context Protocol server that enables Claude to access Google's Gemini AI models through specialized tools for AI-assisted development workflows.

📖 Documentation Overview

This documentation is organized into four main categories to serve different audiences and use cases:

🚀 For End Users

• Installation Guide - Set up the server locally or with Docker
• Configuration - Configure the server for your environment
• Troubleshooting - Common issues and solutions

🛠️ For Developers

• Development Setup - Set up your development environment
• Development Workflows - Git workflows, testing, and collaboration patterns
• Code Style Guide - Coding standards and best practices
• Testing Strategy - Testing approaches and quality assurance
• Test Structure Analysis - Detailed analysis of existing test suite
• Repository Overview - Understanding the codebase structure

🏗️ For System Architects

• Architecture Overview - High-level system design and components
• Component Details - Detailed component descriptions and interactions
• Data Flow Patterns - How data moves through the system
• Architecture Decisions - Architecture Decision Records (ADRs)

🔧 For API Users

• MCP Protocol - Model Context Protocol implementation details
• Tool Reference - Individual tool API documentation

🎯 Quick Start Paths

New User Journey

1. Install the Server → Get up and running quickly
2. Configure Your Setup → Customize for your environment
3. Start Using Tools → Explore AI-assisted workflows
4. Troubleshoot Issues → Resolve common problems

Developer Journey

1. Set Up Development → Prepare your dev environment
2. Understand the Codebase → Navigate the repository
3. Follow Workflows → Git, testing, and collaboration
4. Code Quality Standards → Maintain code quality

Architect Journey

1. System Overview → Understand the high-level design
2. Component Architecture → Deep dive into system parts
3. Data Flow Analysis → Trace information flow
4. Decision Context → Understand design choices

🛠️ Tool Reference

The server provides six specialized tools for different AI collaboration scenarios:

Tool	Purpose	Best For	Documentation
chat	Quick questions, brainstorming	Immediate answers, idea exploration	Low complexity, fast iteration
thinkdeep	Complex analysis, strategic planning	Architecture decisions, system design	High complexity, deep analysis
analyze	Code exploration, system understanding	Codebase comprehension, dependency analysis	Medium complexity, systematic exploration
codereview	Code quality, security, bug detection	PR reviews, security audits	Quality assurance, comprehensive validation
debug	Root cause analysis, error investigation	Bug fixing, performance issues	Problem-solving, systematic debugging
precommit	Automated quality gates	Pre-commit validation, change analysis	Quality gates, automated validation

Tool Selection Guide

For Quick Tasks: Start with chat for immediate answers and brainstorming For Complex Planning: Use thinkdeep for architecture and strategic decisions
For Code Understanding: Use analyze to explore and understand existing code For Quality Assurance: Use codereview and precommit for validation For Problem Solving: Use debug for systematic error investigation

🔄 Collaboration Framework

This project follows the CLAUDE.md Collaboration Framework which defines:

• Tool Selection Matrix: Guidelines for choosing the right tool for each task
• Memory Bank Integration: Context preservation across development sessions
• Quality Gates: Mandatory validation and review processes
• Documentation Standards: Comprehensive documentation requirements

Key Collaboration Patterns

• Complex Tasks (>3 steps): Always use TodoWrite to plan and track progress
• Architecture Decisions: Must involve thinkdeep for exploration before implementation
• Code Reviews: All significant changes require codereview analysis before committing
• Documentation Updates: Any code change must include corresponding documentation updates

📚 Additional Resources

Configuration Examples

• macOS Setup - Local development on macOS
• WSL Setup - Windows Subsystem for Linux
• Docker Setup - Container-based deployment

Project Information

• Main README - Project overview and quick start
• Contributing Guidelines - How to contribute to the project
• Quick Contributing Guide - Simple guide for new contributors
• License - MIT License details
• Collaboration Framework - Development collaboration patterns

Memory Bank System

The project uses a Memory Bank system for context preservation:

• Product Context - Project goals and architecture
• Active Context - Current development status
• Decision Log - Architectural decisions and rationale
• Progress Tracking - Task completion and milestones

🎨 Documentation Standards

For Technical Audiences

• Code Context: All explanations include specific file and line number references (file_path:line_number)
• Architecture Focus: Explain why decisions were made, not just what was implemented
• Data Flow: Trace data through the system with concrete examples
• Error Scenarios: Document failure modes and recovery strategies

For Non-Technical Audiences

• Plain Language: Avoid jargon, explain technical terms when necessary
• Purpose-Driven: Start with "what problem does this solve?"
• Visual Aids: Use diagrams and flowcharts where helpful
• Practical Examples: Show real usage scenarios

🔍 Finding What You Need

By Role

• System Administrators: Start with Installation and Configuration
• End Users: Begin with Tool Reference and Quick Start
• Developers: Follow the Developer Journey starting with Development Setup
• Architects: Review Architecture Overview and System Design

By Task

• Setting Up: Installation → Configuration
• Using Tools: Tool Reference → Specific tool documentation
• Developing: Setup → Workflows → Code Style
• Understanding Architecture: Overview → Components → Data Flow
• Troubleshooting: Troubleshooting Guide or relevant tool documentation

By Problem Type

• Installation Issues: Installation Guide and Troubleshooting
• Configuration Problems: Configuration Guide
• Tool Behavior Questions: Specific Tool Documentation
• Development Questions: Contributing Guides
• Architecture Questions: Architecture Documentation

📝 Contributing to Documentation

This documentation follows the standards defined in CLAUDE.md:

1. Accuracy: Documentation must reflect actual code behavior
2. Completeness: Cover all user-facing functionality
3. Accessibility: Understandable by intended audience
4. Currency: Updated with every related code change

To contribute:

1. Follow the Development Workflows
2. Maintain Code Style Standards
3. Include comprehensive Testing
4. Update relevant documentation sections

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Need Help? Check the Troubleshooting Guide or explore the specific documentation section for your use case. For development questions, start with the Contributing Guidelines.