workflow-intelligence-mcp

Workflow Intelligence MCP

An intelligent development assistant that learns your patterns, automates workflows, and helps manage complex projects through persistent memory and cross-tool orchestration.

🎯 Core Philosophy

Not a domain expert, but a workflow expert.

Instead of replacing specialized tools, this MCP makes you more effective with the tools you already know and love by:

• 🧠 Remembering your development patterns
• 🔄 Automating repetitive workflows
• 🏗️ Scaffolding projects intelligently
• 📝 Generating contextual code
• 🔗 Orchestrating multiple tools
• 📊 Monitoring and suggesting improvements

📚 Documentation

• Getting Started - Installation and first steps
• Architecture - Technical design and components
• Contributing - Development guidelines and principles
• Examples - Real workflow demonstrations
• Lessons Learned - Complete archive of what NOT to do with MCPs

🚀 Key Features

1. Project Memory & Context

• Learns your preferred project structures
• Remembers coding patterns and tool choices
• Tracks analysis workflows and methodologies
• Maintains configuration preferences

2. Intelligent Scaffolding

• Creates project templates based on your history
• Sets up environments with your preferred tools
• Configures git, dependencies, and documentation
• Adapts structure to project type

3. Workflow Automation

• Automates repetitive development tasks
• Suggests logical commit groupings
• Manages file organization
• Orchestrates analysis pipelines

4. Smart Code Generation

• Generates code that matches your style
• Uses your preferred libraries and patterns
• Creates documentation in your format
• Maintains consistency across projects

5. Development Monitoring

• Watches for code duplication opportunities
• Suggests refactoring when files get complex
• Tracks time spent on different tasks
• Recommends workflow optimizations

🛠️ MCP Tools

Core Workflow Tools

• create_project_template - Intelligent project scaffolding
• analyze_workflow_patterns - Learn from existing projects
• suggest_improvements - Development optimization recommendations
• manage_git_workflow - Intelligent commit and branch management
• generate_contextual_code - Code that fits your patterns

Memory & Learning Tools

• remember_pattern - Store workflow preferences
• recall_context - Retrieve project history and decisions
• update_preferences - Adapt to changing patterns
• export_knowledge - Share learned patterns across projects

Integration Tools

• orchestrate_pipeline - Run multi-step workflows
• monitor_development - Track development patterns
• sync_configurations - Keep settings consistent
• generate_documentation - Auto-create project docs

🔧 Installation & Setup

# Clone the repository
git clone <repo-url>
cd workflow_intelligence_mcp

# Install dependencies
pip install -r requirements.txt

# Configure MCP server
python setup.py install

# Test the installation
python test_workflow_mcp.py

📊 Example Workflows

Creating a New Data Science Project

User: "Create a spatial analysis project for health research"

MCP: "Based on your previous projects, I'll set up:
✅ Standard data science structure with spatial focus
✅ Environment with PySAL, GeoPandas, Jupyter
✅ Git with your preferred .gitignore patterns
✅ README template with ESDA workflow
✅ Config files for your analysis parameters
✅ Makefile with common spatial analysis tasks

🧠 Remembered: You prefer Queen contiguity weights, 
row standardization, and significance testing at α=0.05"

Intelligent Development Monitoring

MCP: "I've been watching your development:

📊 Pattern Analysis:
- You've imported the same spatial utilities in 4 notebooks
- Suggestion: Create src/spatial_utils.py module

⏱️ Time Analysis:
- 2.5 hours on clustering analysis
- Suggestion: Commit current progress before trying new approaches

📁 Structure Analysis:
- Your notebook has 150+ cells
- Suggestion: Split into data prep → analysis → visualization stages"

Smart Git Management

MCP: "I see 7 modified files across 3 analysis themes:

📦 Suggested Commits:
1. 'Add spatial autocorrelation analysis'
   - notebooks/02_spatial_autocorr.ipynb
   - src/analysis/moran.py
   
2. 'Update clustering methodology' 
   - notebooks/03_clustering.ipynb
   - src/analysis/clustering.py
   - tests/test_clustering.py

3. 'Improve visualization functions'
   - src/viz/choropleth.py
   - notebooks/04_visualization.ipynb

Would you like me to stage these commits with descriptive messages?"

🎯 Value Proposition

vs GitHub Copilot

• Copilot: Great code suggestions, no project memory
• Workflow MCP: Remembers your patterns, suggests project-level improvements

vs IDE Extensions

• Extensions: Generic functionality
• Workflow MCP: Personalized to your specific development patterns

vs Scripts/Automation

• Scripts: Static, one-size-fits-all
• Workflow MCP: Adaptive, learns and evolves with your workflow

🚀 Getting Started

1. Initialize with existing project: analyze_workflow_patterns /path/to/existing/project
2. Create new project: create_project_template --type spatial_analysis --name my_project
3. Start monitoring: monitor_development --enable-suggestions

📈 Roadmap

Phase 1: Core Memory & Scaffolding

• Project pattern analysis
• Template generation
• Basic memory storage
• Git workflow intelligence

Phase 2: Advanced Monitoring

• Real-time development watching
• Code duplication detection
• Workflow optimization suggestions
• Time tracking and analysis

Phase 3: Cross-Project Learning

• Pattern sharing across projects
• Team workflow synchronization
• Best practice recommendations
• Automated documentation generation

🤝 Contributing

This MCP learns from usage patterns. The more developers use it, the smarter it becomes at recognizing effective workflows and suggesting improvements.

📄 License

MIT License - See LICENSE file for details.