MCP Resume Server

- 📄 Interactive Resume Chat- Ask any question about Michael's background and experience
- 🎯 Smart Context Retrieval- Intelligent context selection based on user questions
- 📄 PDF Download- Generate and download professional CV on demand
- 🎯 Smart Matching- Job description analysis for recruiter insights

- 🤖 Multiple LLM Providers- OpenRouter (free models), OpenAI, Ollama (local)
- ⚡ Real-time Responses- Instant responses with intelligent context retrieval
- 🆓 Free Models Available- Use powerful open-source models at no cost
- 🔧 Flexible Configuration- Easy switching between different AI providers

- 💬 Modern Chat UI- Beautiful Streamlit interface with quick action buttons
- 📱 Mobile Responsive- Works perfectly on all devices and screen sizes
- ✅ System Status- Real-time status indicator shows when all systems are ready
- 🔧 Auto-Configuration- Smart setup flow with contextual help
- ⚡ Quick Actions- One-click access to common tasks and insights

- 🏗️ Purpose-Based Architecture- Professional project organization with logical file grouping
- 🆓 Free Deployment- Deploy on Streamlit Cloud for free
- 📦 Modular Design- Clean separation of concerns for maintainability
- 🔧 Easy Customization- Simple to adapt for your own resume and branding

- ✅ Full EU AI Act Compliance- Complete implementation of all high-risk AI system requirements
- 🔍 Advanced Monitoring- Real-time compliance monitoring with automated alerting
- 📋 Audit Procedures- Comprehensive audit framework with standardized checklists
- 📊 Performance Analytics- Advanced analytics with trend analysis and insights
- 🔍 Conformity Assessment- Systematic assessment procedures for certification readiness
- 📋 Certification Preparation- Complete document management for regulatory submission
- ✅ Compliance Validation- Automated and manual validation with certification eligibility

Coming soon: Interactive screenshots and demo GIFs

- 💬 Chat with the AI- Ask questions about experience, skills, and projects
- 📄 Download CV- Generate professional PDF resumes
- 🎯 Smart Matching- Analyze job descriptions for fit
- ⚡ Quick Actions- Get instant insights with one-click buttons

# 1. Clone the repository git clone https://github.com/michaelwybraniec/mcp-resume.git cd mcp-resume # 2. Create virtual environment (recommended) python -m venv venv source venv/bin/activate # On Windows: venv\Scripts\activate # 3. Install dependencies pip install -r requirements.txt # 4. Run the application streamlit run app.py

💡 Pro Tip:Use the free modelmeta-llama/llama-3.1-8b-instruct:freefor testing!

git add . git commit -m "Deploy AI Resume to Streamlit Cloud" git push origin main

For enhanced LLM providers, add secrets in Streamlit Cloud dashboard:

# Install Ollama brew install ollama # macOS # or visit https://ollama.ai for other platforms # Start Ollama ollama serve # Install models ollama pull llama3.2 ollama pull llama3.1

mcp-resume/ ├── 🏠 app.py # Main entry point (Streamlit-ready) ├── 📋 requirements.txt # Dependencies │ ├── 🔧 core/ # Foundation & Configuration │ ├── config.py # Settings & constants │ └── models.py # Data models & types │ ├── ⚙️ services/ # Business Logic & Integrations │ ├── resume_service.py # Resume data handling │ ├── llm_providers.py # AI/LLM integrations │ ├── document_generator.py # PDF generation │ └── fallback_resume.py # Data fallback service │ ├── 🎨 ui/ # User Interface Layer │ ├── ui_components.py # UI components & styling │ └── session_manager.py # Session state management │ ├── 📊 data/ # Data Files │ ├── resume.json │ ├── michael_wybraniec_resume.json │ └── CV_Michael_Wybraniec_15_Jun_2025.pdf │ └── 🔨 utils/ # Utility Scripts └── create_gist.py # GitHub Gist utilities

┌─────────────────────────────────────────────────────────────────┐ │ 🌐 Streamlit App │ ├─────────────────────────────────────────────────────────────────┤ │ 🎨 UI Layer (ui/) │ │ ├── ui_components.py # Chat interface, modals, styling │ │ └── session_manager.py # State management & initialization │ ├─────────────────────────────────────────────────────────────────┤ │ ⚙️ Services Layer (services/) │ │ ├── resume_service.py # Data retrieval & context │ │ ├── llm_providers.py # AI/LLM integrations │ │ ├── document_generator.py # PDF generation │ │ └── fallback_resume.py # Data fallback service │ ├─────────────────────────────────────────────────────────────────┤ │ 🔧 Core Layer (core/) │ │ ├── config.py # Settings & constants │ │ └── models.py # Data models & types │ ├─────────────────────────────────────────────────────────────────┤ │ 📊 Data Layer (data/) │ │ ├── resume.json # Primary resume data │ │ ├── michael_wybraniec_resume.json # Backup data │ │ └── CV_Michael_Wybraniec_15_Jun_2025.pdf # Professional CV │ └─────────────────────────────────────────────────────────────────┘ │ ▼ ┌─────────────────────────────────────────────────────────────────┐ │ 🤖 External LLM Providers │ ├─────────────────────────────────────────────────────────────────┤ │ 🌐 OpenRouter API │ 🏠 Ollama Local │ 🔑 OpenAI API │ │ • Free models │ • llama3.2 │ • GPT models │ │ • Paid models │ • llama3.1 │ • Advanced AI │ │ • Rate limiting │ • Local processing │ • High quality │ └─────────────────────────────────────────────────────────────────┘

User Input → UI Components → Session Manager → Resume Service ↓ ↓ ↓ ↓ LLM Providers ← Message Processing ← Context Generation ← JSON Data ↓ AI Response → UI Components → User Interface

This AI Resume system isfully compliantwith the EU AI Act regulations for high-risk AI systems. The system has been designed and implemented with comprehensive compliance measures across all required areas.

- ✅AI Transparency Notices- Clear notices about AI system usage and limitations
- ✅Human Oversight Dashboard- Real-time monitoring of flagged responses
- ✅Response Flagging System- Users can flag AI responses for human review
- ✅User Rights Information- Comprehensive information about user rights and system capabilities

- ✅Risk Management System- Automated risk identification, assessment, and mitigation
- ✅Data Governance Framework- Data quality assessment and processing record management
- ✅Technical Documentation- Complete system architecture and compliance documentation
- ✅Record Keeping System- Comprehensive user interaction logging and audit trails

- ✅Advanced Monitoring- Real-time compliance monitoring with automated alerting
- ✅Audit Procedures- Comprehensive audit framework with standardized checklists
- ✅Performance Analytics- Advanced analytics with trend analysis and insights
- ✅Compliance Alerting- Intelligent alert system with threshold-based notifications

Phase 4: Conformity Assessment & Certification

- ✅Conformity Assessment Framework- Systematic assessment procedures with standardized criteria
- ✅Certification Preparation- Complete document management and application procedures
- ✅Compliance Validation- Automated and manual validation with certification readiness
- ✅Regulatory Documentation- Prepared documentation for regulatory submission

The system includes a comprehensive compliance dashboard accessible through the sidebar that provides:

- Real-time Compliance Status- Live monitoring of all compliance systems
- Risk Management Metrics- Risk levels, mitigation status, and trend analysis
- Data Governance Status- Data quality scores and processing compliance
- Audit Trail Monitoring- System operation logs and audit trail integrity
- Performance Analytics- Compliance KPIs and trend analysis
- Certification Readiness- Real-time assessment of certification eligibility

┌─────────────────────────────────────────────────────────────────┐ │ ⚖️ AI Act Compliance Layer │ ├─────────────────────────────────────────────────────────────────┤ │ 🔍 Advanced Monitoring │ 📋 Audit Procedures │ │ • Real-time monitoring │ • Standardized checklists │ │ • Automated alerting │ • Comprehensive reporting │ │ • Performance analytics │ • Compliance verification │ ├─────────────────────────────────────────────────────────────────┤ │ 🔍 Conformity Assessment │ 📋 Certification Preparation │ │ • Systematic procedures │ • Document management │ │ • Standardized criteria │ • Application workflows │ │ • Certification readiness │ • Regulatory submission │ ├─────────────────────────────────────────────────────────────────┤ │ ⚠️ Risk Management │ 📊 Data Governance │ │ • Risk identification │ • Data quality management │ │ • Assessment procedures │ • Processing records │ │ • Mitigation strategies │ • Compliance monitoring │ ├─────────────────────────────────────────────────────────────────┤ │ 📝 Record Keeping │ ✅ Compliance Validation │ │ • System operation logs │ • Automated validation rules │ │ • Audit trails │ • Manual validation procedures │ │ • Retention management │ • Certification eligibility │ └─────────────────────────────────────────────────────────────────┘

- AI_ACT_COMPLIANCE.md- Comprehensive compliance documentation
- services/risk_management.py- Risk management system implementation
- services/data_governance.py- Data governance and quality management
- services/record_keeping.py- Record keeping and audit trail system
- services/compliance_monitoring.py- Advanced monitoring and alerting
- services/audit_procedures.py- Audit procedures and protocols
- services/performance_analytics.py- Performance analytics and insights
- services/conformity_assessment.py- Conformity assessment framework
- services/certification_preparation.py- Certification preparation system
- services/compliance_validation.py- Compliance validation procedures

- ✅ Regulatory Compliance- Full adherence to EU AI Act requirements
- ✅ Risk Mitigation- Comprehensive risk management and monitoring
- ✅ Transparency- Clear AI system information and user rights
- ✅ Human Oversight- Effective human control and review mechanisms
- ✅ Audit Readiness- Complete documentation and audit trails
- ✅ Certification Ready- Prepared for official compliance certification

- core/config.py- Configuration, constants, and settings
- core/models.py- Data models and type definitions

- ui/ui_components.py- UI components, styling, and modals
- ui/session_manager.py- Session state management and initialization

- services/resume_service.py- Resume data retrieval and context generation
- services/llm_providers.py- LLM provider implementations and unified chat interface
- services/document_generator.py- PDF generation and export functionality
- services/fallback_resume.py- Resume data service and context management

- data/- Resume JSON files and PDF assets
- utils/- Utility scripts and helper functions

- "Tell me about Michael's work experience"
- "What are his technical skills?"
- "Summarize his background for a recruiter"
- "Find projects involving JavaScript"
- "What's his experience with AI and machine learning?"
- "How many years of Python experience does he have?"
- "Has he worked with AI/ML technologies?"
- "What industries has he worked in?"

- 👤 Summarize Profile- Get comprehensive candidate overview
- 📅 Years Experience- View career progression timeline
- 🛠️ Technical Skills- Analyze technical competencies
- 🎯 Smart Match- Job fit analysis with match scores
- 📄 Download CV- Professional PDF resume

- "Is this candidate suitable for a senior developer role?"
- "What's their leadership experience?"
- "Do they have experience with cloud platforms?"
- "Rate their frontend vs backend skills"
- "Analyze fit for this job description..."(paste job description)

- Clean, professional design with expanded sidebar
- Quick action buttons for instant insights
- Real-time system status indicator
- Message timestamps and processing indicators
- Mobile-responsive design

- Intelligent context selection based on user questions
- Focused responses with relevant resume sections
- Experience, skills, projects, and achievements matching
- Job description analysis capabilities

- Generate PDF CV on demand
- Clean, professional formatting
- Download directly from the interface

- System Status: Real-time indicator showing "All Systems Ready!" when configured
- Help & Tips: Comprehensive setup guide and sample questions
- Quick Actions: Organized, expandable panel for common tasks
- Auto-Setup: Smart configuration flow with contextual guidance

1.Streamlit Cloud (Recommended - Free)⭐

- Free hosting for public repositories
- Automatic deployments from GitHub
- Built-in secrets management
- Perfect for demos and portfolios

pip install -r requirements.txt streamlit run app.py

- Railway, Render, Heroku
- Use providedrequirements.txtandruntime.txt
- Set startup command:streamlit run app.py --server.port $PORT

mcp-resume/ ├── 🏠 app.py # Main entry point (Streamlit-ready) ├── 📋 requirements.txt # Python dependencies ├── 🐍 runtime.txt # Python version for deployment ├── 📖 README.md # Documentation ├── 🚀 DEPLOYMENT.md # Deployment guide ├── 🔧 secrets.toml.example # Example secrets configuration ├── ⚖️ AI_ACT_COMPLIANCE.md # AI Act compliance documentation ├── 📋 agentic-sdlc/ # Agentic SDLC project management │ ├── tasks/ # Task management and tracking │ └── project-backlog.md # Project backlog and progress │ ├── 🔧 core/ # Foundation & Configuration │ ├── __init__.py │ ├── config.py # Settings & constants │ └── models.py # Data models & types │ ├── ⚙️ services/ # Business Logic & Integrations │ ├── __init__.py │ ├── resume_service.py # Resume data handling │ ├── llm_providers.py # AI/LLM integrations │ ├── document_generator.py # PDF generation │ ├── fallback_resume.py # Data fallback service │ ├── risk_management.py # AI Act risk management system │ ├── data_governance.py # Data governance and quality management │ ├── record_keeping.py # Record keeping and audit trails │ ├── compliance_monitoring.py # Advanced compliance monitoring │ ├── audit_procedures.py # Audit procedures and protocols │ ├── performance_analytics.py # Performance analytics and insights │ ├── conformity_assessment.py # Conformity assessment framework │ ├── certification_preparation.py # Certification preparation system │ └── compliance_validation.py # Compliance validation procedures │ ├── 🎨 ui/ # User Interface Layer │ ├── __init__.py │ ├── ui_components.py # UI components & styling │ └── session_manager.py # Session state management │ ├── 📊 data/ # Data Files │ ├── resume.json # Primary resume data │ ├── michael_wybraniec_resume.json # Backup resume data │ ├── CV_Michael_Wybraniec_15_Jun_2025.pdf # Professional CV │ ├── risk_management_log.json # Risk management data │ ├── data_governance_log.json # Data governance records │ ├── system_records.json # System operation logs │ ├── audit_trails.json # Audit trail data │ ├── compliance_monitoring.json # Compliance monitoring data │ ├── audit_procedures.json # Audit procedures data │ ├── performance_analytics.json # Performance analytics data │ ├── conformity_assessment.json # Conformity assessment data │ ├── certification_preparation.json # Certification preparation data │ └── compliance_validation.json # Compliance validation data │ ├── 🔨 utils/ # Utility Scripts │ ├── __init__.py │ └── create_gist.py # GitHub Gist utilities │ └── 🐍 venv/ # Virtual environment

- Easy Navigation: Find files by purpose (UI, services, data, etc.)
- Maintainable: Clear separation of concerns across directories
- Scalable: Add new features without touching existing modules
- Testable: Each layer can be tested independently
- Professional: Industry-standard project organization

- New LLM Providers: Extendservices/llm_providers.py
- UI Changes: Modifyui/ui_components.py
- Export Formats: Add toservices/document_generator.py
- Data Sources: Extendservices/resume_service.py
- Configuration: Updatecore/config.py

- Streamlit Compatible:app.pystays in root for deployment
- Logical Grouping: Files organized by functionality, not arbitrarily
- Clean Root: No clutter - only essential files visible
- Python Packages: Proper__init__.pyfiles for clean imports

- 88% reductionin main file complexity (1,500 → 180 lines)
- 8 focused modulesinstead of monolithic structure
- Clear dependenciesand import relationships
- Single responsibilityprinciple throughout

git clone <your-repo> cd mcp-resume pip install -r requirements.txt