Manages environment variables through .env files for secure API key storage and configuration
Provides file management capabilities including README creation, content appending, and PDF text extraction
Fetches song lyrics from the Genius API using artist and song name searches
Integrates with Google Gemini AI models for content analysis, README summarization, lyrics interpretation, and intelligent question-answering for PDF documents
Provides AI application framework capabilities for document chunking, text processing, and question-answering chains
Built on Python runtime with support for PDF processing, text extraction, and web application functionality
Specialized tools for README file management including automated creation, content generation, and documentation maintenance
Creates interactive web applications for PDF document upload, chatbot interfaces, and song lyrics analysis
gitSERVER - MCP Server for README Management
A Model Context Protocol server for managing README files in development projects.
Overview
gitSERVER is a FastMCP-based server that streamlines README file management through the Model Context Protocol. It provides automated README creation, content generation, summarization, and MCP client integration.
Features
- Automatic README file creation when files do not exist
- Content management with append functionality
- README content summarization and analysis
- MCP resource integration for content access
- Intelligent prompt generation for README analysis
- Robust error handling with fallback mechanisms
Installation
Prerequisites
- Python 3.7 or higher
- FastMCP library
Setup Steps
- Install dependency: pip install fastmcp
- Save main.py to your project directory
- Start the server: python main.py
Usage
MCP Tools
create_file(response: str)
- Purpose: Generate and append content to README file
- Parameter: response (string) - content to add to README
- Returns: Confirmation message
- Use case: Adding structured documentation content
sumamrize_readme()
- Purpose: Read complete README file content
- Parameters: None
- Returns: Full README content or empty file message
- Use case: Content review and analysis
MCP Resources
README://content
- Provides direct access to README file content
- Uses MCP resource access pattern
- Allows MCP clients to fetch README content
MCP Prompts
readme_summary()
- Generates prompts for README summarization
- Returns contextual prompt or empty file message
- Detects empty files automatically
Project Structure
your-project/ main.py (MCP server implementation) README.md (Auto-generated README file) other-files (Your project files)
How It Works
File Management
- Detects existing README.md files in project directory
- Creates empty README.md if none exists
- Safely appends new content while preserving existing data
- Ensures all file operations complete successfully
MCP Integration
- Tools: Direct function calls for README operations
- Resources: Resource-based README content access
- Prompts: Contextual prompt generation for AI interactions
Technical Details
File Operations
- Safe file handling with proper open/close operations
- Content stripping to remove unnecessary whitespace
- Fallback messages for empty or missing files
Error Handling
- Creates README.md automatically when needed
- Returns user-friendly messages for empty content
- Handles file operation exceptions gracefully
API Reference
Tool Functions:
- create_file(response): Append content to README
- sumamrize_readme(): Retrieve README content
Resource Endpoints:
- README://content: Direct README content access
Prompt Generators:
- readme_summary(): Context-aware README summarization
Use Cases
- Documentation automation and maintenance
- README content analysis for improvements
- New project setup with proper documentation
- MCP workflow integration for README management
Development
Contributing
- Fork the repository
- Create feature branch
- Implement changes
- Test with MCP clients
- Submit pull request
Testing Requirements
Test that your MCP client can:
- Call create_file tool successfully
- Retrieve content via sumamrize_readme
- Access README://content resource
- Generate prompts with readme_summary
Compatibility
- MCP Protocol: Standard MCP client compatible
- Python: Requires version 3.7 or higher
- Dependencies: Only requires FastMCP library
License
Open source project. Check repository for license details.
Support
For issues or questions:
- Check project repository for existing issues
- Create new issues for bugs or features
- Refer to FastMCP documentation for MCP questions
Note: This is a Model Context Protocol server. You need an MCP-compatible client to interact with the server effectively.
Google Gemini PDF Chatbot
A Streamlit web application for uploading PDF documents and asking questions about their content using Google Gemini AI.
Overview
This chatbot application uses Google Gemini 1.5 Flash model to provide intelligent question-answering for uploaded PDF documents. Users upload PDF files, the app processes content, and allows questions about the document.
Features
- PDF Upload Support: Upload and process PDF documents
- Text Extraction: Automatically extracts text from PDF files
- Intelligent Chunking: Splits large documents into manageable chunks
- AI-Powered Q&A: Uses Google Gemini 1.5 Flash for accurate answers
- Interactive Web Interface: Clean Streamlit interface
- Real-time Processing: Instant responses to user queries
Installation
Prerequisites
- Python 3.7 or higher
- Google API key for Gemini AI
- Required Python packages
Setup Instructions
- Clone repository and navigate to project directory
- Install dependencies: pip install streamlit python-dotenv PyPDF2 langchain langchain-google-genai
- Create .env file in project root: GOOGLE_API_KEY=your_google_api_key_here
- Get Google API Key:
- Visit Google AI Studio or Google Cloud Console
- Create or select project
- Enable Gemini API
- Generate API key and add to .env file
Usage
Running the Application
- Start Streamlit app: streamlit run app.py
- Access application:
- Open web browser
- Navigate to http://localhost:8501
Using the Chatbot
- Upload Document:
- Click file uploader
- Select PDF file
- Wait for processing
- Ask Questions:
- Type question in text input field
- Press Enter
- AI analyzes document and provides answer
Supported File Types
- PDF files (.pdf) - Fully implemented
- Text files (.txt) - Declared support
- Word documents (.docx) - Declared support
Technical Architecture
Core Components
- Streamlit Frontend: Web interface for uploads and interaction
- PDF Processing: PyPDF2 extracts text from documents
- Text Chunking: LangChain CharacterTextSplitter breaks large texts
- AI Integration: Connects to Google Gemini via LangChain
- Question Answering: LangChain QA chain for document-based answers
Processing Flow
- User uploads PDF document
- Application extracts text from all pages
- Text split into chunks (1000 chars with 200 char overlap)
- Chunks converted to LangChain Document objects
- User submits question
- QA chain processes question against document chunks
- Gemini AI generates and returns answer
Configuration
Environment Variables
- GOOGLE_API_KEY: Your Google API key for Gemini AI services
Text Splitter Settings
- Chunk Size: 1000 characters
- Chunk Overlap: 200 characters
- Separator: Newline character
AI Model Configuration
- Model: Google Gemini 1.5 Flash
- Chain Type: stuff (processes all chunks together)
Project Structure
project/ app.py (Main Streamlit application) .env (Environment variables - not in repo) requirements.txt (Python dependencies) README.md (Project documentation)
Dependencies
Required Python Packages:
- streamlit: Web application framework
- python-dotenv: Environment variable management
- PyPDF2: PDF text extraction
- langchain: AI application framework
- langchain-google-genai: Google Gemini integration
Installation: pip install streamlit python-dotenv PyPDF2 langchain langchain-google-genai
Troubleshooting
Common Issues
- API Key Errors:
- Ensure Google API key is correctly set in .env file
- Verify API key has access to Gemini AI services
- PDF Processing Issues:
- Some PDFs may have text as images (not supported)
- Encrypted PDFs may require additional handling
- Memory Issues:
- Large PDF files may consume significant memory
- Consider file size limits for production use
Error Handling
Application includes error handling for:
- Missing text content in PDF pages
- API key configuration issues
- File upload validation
Contributing
- Fork the repository
- Create feature branch
- Make changes
- Test with various PDF files
- Commit changes
- Push to branch
- Create Pull Request
License
Open source project. Check LICENSE file for details.
Acknowledgments
- Google AI for Gemini AI model
- LangChain team for AI application framework
- Streamlit team for web app framework
Support
For issues or questions:
- Create issue in project repository
- Check existing documentation first
- Provide detailed environment and issue information
Note: Requires valid Google API key and internet connection. Ensure proper permissions for Google Gemini AI services. This is a Python-based web application built with Streamlit that allows users to input a song name and artist name, fetch the lyrics using the Genius API, and then analyze the meaning of those lyrics using Google's Gemini AI model.
The project consists of three main files:
- app.py - The main Streamlit application that provides the user interface
- genius_lyrics.py - Handles fetching lyrics from the Genius API using the lyricsgenius library
- lyrics_meaning.py - Uses Google's Gemini AI to provide detailed line-by-line analysis of the lyrics
Key features:
- Clean, intuitive web interface with song search functionality
- Integration with Genius API for accurate lyrics retrieval
- AI-powered analysis using Google's Gemini 2.5 Flash model for deep lyric interpretation
- Expandable lyrics viewer with download option to save lyrics as text files
- Streaming AI analysis for better user experience
- Error handling for missing lyrics and API failures
- Session state management to maintain data across interactions
The application requires API keys for both Genius and Google's Gemini AI service, which should be stored in environment variables. Users can search for any song, view the complete lyrics, download them, and get detailed AI analysis explaining metaphors, cultural references, and emotional meanings.
This server cannot be installed
remote-capable server
The server can be hosted and run remotely because it primarily relies on remote services or has no dependency on the local environment.
Enables automated README file management for development projects through MCP tools for content creation and summarization, resources for direct file access, and prompts for AI-powered documentation analysis.