sreerajchintham/my-first-mcp-server

🚀 MCP Multi-Service Server

A comprehensive Model Context Protocol (MCP) server that integrates Google Colab, GitHub, and Google Docs with AI-powered analysis for seamless development workflow automation.

🌟 Features

🔗 Multi-Platform Integration

• Google Colab - Notebook management and analysis
• GitHub - Repository operations and code insights
• Google Docs - Document management and resume building
• AI Analysis - Powered by Google Gemini API

🤖 AI-Powered Capabilities

• Smart README Generation - Create professional documentation from notebooks
• Code Analysis - Comprehensive repository insights and summaries
• Resume Building - Auto-generate project summaries for professional profiles
• Intelligent Parsing - Extract libraries, dependencies, and project structure

⚡ Automation Features

• One-Click Repository Creation - Convert Colab notebooks to GitHub repos
• Interactive Testing - Comprehensive test suite with user-friendly interface
• Batch Processing - Handle multiple files and repositories efficiently
• Fallback Modes - Graceful degradation when APIs are unavailable

🚀 Quick Start

1. Clone and Install

git clone <https://github.com/sreerajchintham/my-first-mcp-server>
cd my-first-mcp-server
pip install -r requirements.txt

2. Configure APIs

# Set up Google APIs
python reauthorize_google_apis.py

# Create .env file
cp .env.example .env
# Edit .env with your API keys

3. Run the Server

python mcp_server.py

4. Test Everything

python test_client.py

📋 Prerequisites

• Python 3.8+
• Google Account with Drive access
• GitHub Account with personal access token
• Gemini API Key (optional, for AI features)

🛠️ Installation

Option 1: Development Setup

# Create virtual environment
python -m venv .venv
source .venv/bin/activate  # Linux/Mac
# .venv\Scripts\activate   # Windows

# Install dependencies
pip install -r requirements.txt

Option 2: Production Setup

# Use locked versions for reproducibility
pip install -r requirements-lock.txt

⚙️ Configuration

1. Google APIs Setup

1. Create project in Google Cloud Console
2. Enable Drive API and Docs API
3. Create OAuth 2.0 credentials (Desktop app)
4. Download client_secret.json → CREDENTIALS/client_secret.json
5. Run authorization: python reauthorize_google_apis.py

2. GitHub API Setup

1. Go to GitHub Settings > Personal Access Tokens
2. Create token with repo and user scopes
3. Add to .env file

3. Environment Variables

# .env file
GOOGLE_CREDENTIALS_DIR=./CREDENTIALS
GOOGLE_TOKEN_FILE=token.json
GITHUB_TOKEN=your_github_token_here
GEMINI_API_KEY=your_gemini_api_key_here  # Optional

📚 Usage

Interactive Mode

python test_client.py

• Browse and analyze GitHub repositories
• Generate AI-powered project summaries
• Create professional documentation
• Manage Google Docs integration

Programmatic Usage

from mcp.client.stdio import stdio_client
from mcp import StdioServerParameters

# Connect to MCP server
server_params = StdioServerParameters(
    command="python", 
    args=["mcp_server.py"]
)

# Use MCP tools
async with stdio_client(server_params) as (read, write):
    # ... MCP operations

🔧 Available Tools

Google Colab Tools

• list_colab_files() - List notebooks in Google Drive
• read_colab_notebook(file_id) - Read notebook content
• generate_readme(file_id, file_name) - AI-powered README generation

GitHub Tools

• list_github_repos() - List repositories with filters
• read_github_repo_files(repo_name) - Analyze repository files
• analyze_github_repo_with_ai(repo_name) - AI-powered analysis
• create_github_repo() - Create repo from Colab notebook

Google Docs Tools

• list_google_docs(search_term) - Find Google Docs
• create_google_doc(title, content) - Create new Google Doc
• add_to_google_doc(doc_id, content) - Add content to documents

Utility Tools

• ping() - Test server connectivity
• summarize_repo_analysis_for_resume() - Generate resume summaries

🤖 AI Analysis Types

🔗 Key Features

GitHub Analysis to Google Docs Integration

• Automatic Saving: Save AI-powered repository analysis directly to Google Docs
• Document Options: Create new documents or add to existing ones
• Professional Formatting: Clean, structured content with proper headings
• Interactive Workflow: Prompted options after each analysis
• Search & Select: Find and select existing documents by search term

Enhanced Google Docs Management

• Document Creation: Create new Google Docs with initial content
• Content Addition: Add formatted sections to existing documents
• Document Search: Search and list documents by keywords
• Document Linking: Direct links to created/updated documents

📊 Integration Examples

Claude Desktop Integration

{
  "mcpServers": {
    "multi-service-server": {
      "command": "python",
      "args": ["/path/to/mcp_server.py"],
      "cwd": "/path/to/project",
      "env": {
        "GOOGLE_CREDENTIALS_DIR": "/path/to/CREDENTIALS",
        "GITHUB_TOKEN": "your_token_here"
      }
    }
  }
}

Resume Building Workflow

# 1. Analyze repository
analysis = await analyze_github_repo_with_ai("user/repo", "comprehensive")

# 2. Generate resume summary
summary = await summarize_repo_analysis_for_resume(
    "user/repo", 
    analysis, 
    "technical"
)

# 3. Add to Google Doc
await add_to_google_doc("doc_id", summary, "Projects")

GitHub Analysis to Google Docs Workflow

# 1. Analyze GitHub repository with AI
analysis = await analyze_github_repo_with_ai("user/repo", "comprehensive")

# 2. Option A: Create new document with analysis
doc_result = await create_google_doc(
    "GitHub Analysis - user/repo",
    analysis,
    "Repository Analysis"
)

# 2. Option B: Add analysis to existing document
await add_to_google_doc(
    "existing_doc_id",
    analysis,
    "GitHub Repository Analysis - user/repo"
)

🏗️ Project Structure

my-first-mcp-server/
├── 📄 mcp_server.py              # Main MCP server
├── 🧪 test_client.py             # Interactive test suite
├── 🧪 test_github_tool.py        # GitHub tool testing
├── 🔧 debug_mcp_connection.py    # MCP connection debugging
├── 🔑 reauthorize_google_apis.py # Google OAuth setup
├── ✅ validate_setup.py          # Dependency validation
├── 📋 requirements.txt           # Python dependencies
├── 🔒 requirements-lock.txt      # Locked versions
├── 📖 README.md                  # Project documentation
├── 📖 SETUP.md                   # Detailed setup guide
├── 🌍 .env                       # Environment variables
├── 📁 CREDENTIALS/               # OAuth credentials
│   ├── client_secret.json
│   └── token.json
└── 📁 support files/             # Configuration and support files
    ├── claude_desktop_config.json
    ├── cursor_mcp_config.json
    ├── cursor_mcp_config_final.json
    ├── CURSOR_FIX_GUIDE.md
    ├── CURSOR_SETUP_GUIDE.md
    ├── demo_readme.py
    ├── presentation_outline.md
    ├── render_deployment_guide.md
    └── run_env.py

🔍 Troubleshooting

Common Issues

Debug Mode

import logging
logging.basicConfig(level=logging.DEBUG)

Validation Check

python validate_setup.py

🧪 Testing

Interactive Testing

python test_client.py

README Generation Demo

python demo_readme.py

Environment Verification

python run_env.py

🤝 Contributing

1. Fork the repository
2. Create a feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

📄 License

This project is licensed under the MIT License - see the file for details.

🙏 Acknowledgments

• Model Context Protocol - For the excellent MCP framework
• Google APIs - For comprehensive service integration
• GitHub API - For repository management capabilities
• Google Gemini - For AI-powered analysis features

📞 Support

• 📖 Documentation: Check for detailed setup
• 🐛 Issues: Report bugs via GitHub Issues
• 💬 Discussions: Use GitHub Discussions for questions
• 🧪 Testing: Run python test_client.py for interactive debugging