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.