# Product Requirements Document (PRD)
# GitScribe: Web Scraping RAG MCP Server for Git-based Documentation

## 1. Executive Summary

### Project Overview
**GitScribe** is a Model Context Protocol (MCP) server that enables intelligent web scraping of Git-based documentation with Retrieval Augmented Generation (RAG) capabilities. This tool will help code assistants and developers efficiently extract, process, and retrieve information from documentation websites, GitHub repositories, and other Git-based resources to accelerate application development.

### Product Name
**GitScribe** 📜 - *"Scribing knowledge from the Git universe"*

### Key Value Proposition
- **Automated Documentation Extraction**: Scrape and index documentation from any Git-based source
- **Intelligent Content Retrieval**: RAG-powered search and retrieval for relevant code examples and documentation
- **Developer Productivity**: Accelerate development by providing contextual documentation to code assistants
- **Universal Compatibility**: Works with GitHub, GitLab, Bitbucket, and other Git platforms

## 2. Problem Statement

### Current Challenges
1. **Manual Documentation Search**: Developers spend significant time manually searching through documentation
2. **Context Switching**: Constantly switching between code editor and documentation websites
3. **Inconsistent Information**: Different documentation formats and structures across projects
4. **Limited AI Context**: Code assistants lack access to project-specific documentation
5. **Outdated Information**: Static documentation may not reflect latest changes

### Target Users
- **Software Developers**: Need quick access to API documentation and code examples
- **AI Code Assistants**: Require structured documentation for better code generation
- **DevOps Engineers**: Need infrastructure and deployment documentation
- **Technical Writers**: Want to analyze and improve documentation structure

## 3. Product Goals and Objectives

### Primary Goals
1. **Seamless Documentation Access**: Provide instant access to relevant documentation within development workflow
2. **Intelligent Content Processing**: Use RAG to understand and retrieve contextually relevant information
3. **Universal Git Support**: Support all major Git platforms and documentation formats
4. **Real-time Updates**: Keep documentation synchronized with source repositories

### Success Metrics
- **Response Time**: < 2 seconds for documentation queries
- **Accuracy**: > 90% relevance score for retrieved content
- **Coverage**: Support for 20+ popular documentation frameworks
- **Adoption**: 1000+ active developers using the tool within 6 months

## 4. Technical Requirements

### 4.1 Core Features

#### Web Scraping Engine
- **Beautiful Soup Integration**: Parse HTML content from documentation sites
- **Multi-format Support**: Handle Markdown, HTML, reStructuredText, and other formats
- **Rate Limiting**: Respect robots.txt and implement intelligent throttling
- **Error Handling**: Robust error recovery and retry mechanisms

#### RAG System
- **Vector Database**: Store document embeddings for semantic search
- **Embedding Generation**: Create high-quality embeddings for text chunks
- **Semantic Search**: Find relevant content based on user queries
- **Context Ranking**: Rank results by relevance and freshness

#### MCP Server Architecture
- **Protocol Compliance**: Full MCP specification compliance
- **Resource Management**: Efficient handling of large documentation sets
- **Tool Integration**: Provide tools for searching, indexing, and retrieving content
- **Streaming Support**: Handle large responses with streaming

### 4.2 Data Sources

#### Git-based Platforms
- **GitHub**: Public and private repositories
- **GitLab**: Self-hosted and GitLab.com
- **Bitbucket**: Atlassian-hosted repositories
- **Azure DevOps**: Microsoft repositories

#### Documentation Formats
- **Static Site Generators**: Jekyll, Hugo, Gatsby, VitePress
- **Documentation Platforms**: GitBook, Notion, Confluence
- **API Documentation**: OpenAPI/Swagger, Postman collections
- **Framework Docs**: React, Vue, Angular, Django, Flask, etc.

### 4.3 Technical Architecture

```
┌─────────────────┐    ┌─────────────────┐    ┌─────────────────┐
│   MCP Client    │───▶│   MCP Server    │───▶│  Web Scraper    │
│ (Code Assistant)│    │   (This Tool)   │    │ (Beautiful Soup)│
└─────────────────┘    └─────────────────┘    └─────────────────┘
                                │
                                ▼
                       ┌─────────────────┐
                       │   RAG System    │
                       │  - Vector DB    │
                       │  - Embeddings   │
                       │  - Search       │
                       └─────────────────┘
```

## 5. Functional Requirements

### 5.1 Core Capabilities

#### FR-001: Documentation Discovery
- **Auto-detect** documentation structure in Git repositories
- **Index** documentation files and generate metadata
- **Monitor** for changes and update index automatically

#### FR-002: Content Extraction
- **Parse** HTML, Markdown, and other documentation formats
- **Extract** code examples, API references, and explanatory text
- **Clean** and normalize content for consistent processing

#### FR-003: Semantic Search
- **Query** documentation using natural language
- **Rank** results by relevance and recency
- **Filter** by documentation type, language, or framework

#### FR-004: Content Retrieval
- **Return** relevant documentation chunks with context
- **Provide** source attribution and links
- **Support** follow-up queries and refinement

### 5.2 MCP Tools

#### Tool: `scrape_documentation`
```json
{
  "name": "scrape_documentation",
  "description": "Scrape and index documentation from a Git repository or website",
  "parameters": {
    "url": "Repository or documentation URL",
    "depth": "Maximum crawling depth",
    "formats": "Supported document formats"
  }
}
```

#### Tool: `search_documentation`
```json
{
  "name": "search_documentation",
  "description": "Search indexed documentation using semantic search",
  "parameters": {
    "query": "Natural language search query",
    "limit": "Maximum number of results",
    "filter": "Filter criteria (language, framework, etc.)"
  }
}
```

#### Tool: `get_code_examples`
```json
{
  "name": "get_code_examples",
  "description": "Extract code examples related to a specific topic",
  "parameters": {
    "topic": "Programming topic or concept",
    "language": "Programming language filter",
    "framework": "Framework or library filter"
  }
}
```

## 6. Non-Functional Requirements

### 6.1 Performance
- **Response Time**: < 2 seconds for search queries
- **Throughput**: Handle 100+ concurrent requests
- **Scalability**: Support 10,000+ indexed documents
- **Memory Usage**: < 2GB RAM for standard deployment

### 6.2 Reliability
- **Uptime**: 99.9% availability
- **Error Recovery**: Graceful handling of network failures
- **Data Consistency**: Ensure index consistency across updates
- **Backup**: Regular backups of indexed content

### 6.3 Security
- **Authentication**: Support for private repository access
- **Rate Limiting**: Prevent abuse and respect server limits
- **Data Privacy**: Secure handling of sensitive documentation
- **Access Control**: Role-based access to different documentation sources

## 7. User Stories

### US-001: Developer Documentation Search
**As a** software developer
**I want to** search for API documentation while coding
**So that** I can quickly find relevant examples and usage patterns

### US-002: Code Assistant Enhancement
**As an** AI code assistant
**I want to** access project-specific documentation
**So that** I can provide more accurate and contextual code suggestions

### US-003: Documentation Monitoring
**As a** technical lead
**I want to** monitor documentation updates across repositories
**So that** I can ensure our knowledge base stays current

## 8. Technical Implementation Plan

### Phase 1: Core Infrastructure (Weeks 1-4)
- Set up MCP server framework
- Implement basic web scraping with Beautiful Soup
- Create document parsing and cleaning pipeline
- Set up vector database for embeddings

### Phase 2: RAG Implementation (Weeks 5-8)
- Implement embedding generation
- Build semantic search capabilities
- Create query processing and ranking system
- Add content retrieval and formatting

### Phase 3: Git Integration (Weeks 9-12)
- Add GitHub API integration
- Implement repository monitoring
- Create automated indexing pipeline
- Add support for multiple Git platforms

### Phase 4: Enhancement and Testing (Weeks 13-16)
- Performance optimization
- Comprehensive testing
- Documentation and examples
- Beta user feedback integration

## 9. Dependencies and Constraints

### Technical Dependencies
- **Python 3.9+**: Core runtime environment
- **Beautiful Soup 4**: HTML/XML parsing
- **Requests**: HTTP client for web scraping
- **ChromaDB**: Vector database for embeddings
- **OpenAI/Hugging Face**: Embedding models
- **FastAPI**: Web server framework

### External Constraints
- **Rate Limits**: GitHub API and documentation site limits
- **Memory**: Large documentation sets require significant storage
- **Network**: Dependent on external site availability
- **Compliance**: Must respect robots.txt and ToS

## 10. Success Criteria and KPIs

### Functional Success
- ✅ Successfully scrape documentation from 20+ popular frameworks
- ✅ Achieve < 2 second average response time
- ✅ Maintain > 90% search result relevance
- ✅ Support all major Git platforms

### Business Success
- 📈 1000+ active users within 6 months
- 📈 10,000+ documentation queries per month
- 📈 Positive feedback from 80%+ of beta users
- 📈 Integration with 5+ popular code assistants

## 11. Risk Assessment

### High Risk
- **Rate Limiting**: Documentation sites may block aggressive scraping
- **Content Changes**: Dynamic content may break parsing
- **Performance**: Large-scale indexing may impact response times

### Medium Risk
- **Compatibility**: Different documentation formats require custom parsers
- **Maintenance**: Keeping up with platform API changes
- **Legal**: Copyright and ToS compliance for scraped content

### Mitigation Strategies
- Implement intelligent rate limiting and caching
- Use headless browsers for dynamic content
- Design modular parsers for different formats
- Regular legal review of scraping practices

## 12. Future Enhancements

### Version 2.0 Features
- **Multi-language Support**: Support for non-English documentation
- **Visual Content**: Extract and index diagrams and screenshots
- **Interactive Examples**: Executable code examples
- **Community Features**: User ratings and contributions

### Integration Opportunities
- **IDE Extensions**: VS Code, IntelliJ, Vim plugins
- **CI/CD Integration**: Automated documentation validation
- **Analytics**: Usage patterns and popular queries
- **API Gateway**: RESTful API for broader integration

---

**Document Version**: 1.0  
**Last Updated**: August 2, 2025  
**Author**: AI Assistant  
**Status**: Draft
