ChurnFlow MCP Server v0.4.2

An ADHD-friendly productivity system powered by AI agents, SQLite database, and GitHub Copilot

ChurnFlow is a production-ready Model Context Protocol (MCP) server that transforms the way ADHD minds manage productivity. Built with optional SQLite database integration, GitHub Copilot support, and comprehensive AI assistance, ChurnFlow works with your natural patterns of thinking, capturing, and processing information.

🎉 New in v0.4.2: Advanced Database Features & Migrations

v0.4.1 Highlights:

• Database CLI Commands: Search, analytics, and review query commands

• MCP Database Tools: Expose database features through GitHub Copilot

• Enhanced Dashboard: Database-powered statistics and insights

• Search Interface: Full-text search integration with CLI

• 🗄️ SQLite Database Integration: Optional advanced features with full-text search, analytics, and AI learning

• 🔍 Full-Text Search (FTS5): Search across all captures with ranking and relevance scoring

• 📊 Analytics Dashboard: Track inbox, active, completed, and overdue items with real-time statistics

• 🧠 AI Learning Patterns: Context inference improves over time with user feedback

• 🏗️ Clean Architecture: Database setup separated from capture operations (resolves code smells)

• 📁 Dual Storage: Captures save to both markdown files AND SQLite database

• 🔄 Optional Enhancement: System works perfectly in file-only mode when database not set up

🧠 The Problem

Traditional productivity systems fail ADHD brains because they require too much cognitive overhead:

• Capture friction: Great ideas get lost while driving, in meetings, or during hyperfocus sessions

• Processing overhead: Spending more time organizing tasks than actually doing them

• Context switching pain: Losing track of where you were after interruptions

• System maintenance burden: The productivity system becomes another task to manage

✨ The ChurnFlow Solution

ChurnFlow uses AI to eliminate the cognitive overhead of productivity management:

• 🎤 Frictionless Capture: Voice or text input that automatically infers context and routing

• 🤖 AI-Powered Processing: Natural language understanding that categorizes and prioritizes items

• 📍 Context Awareness: Seamlessly switch between life domains (business, personal, projects)

• 🔄 Automatic Recovery: Get back on track after interruptions without losing momentum

🏗️ Architecture

ChurnFlow is built around three core concepts:

Collections

Domain-specific folders that archive completed work and reference materials:

• gsc-ai/ - AI consulting business

• project-55/ - Personal business empire plan

• tractor/ - Equipment restoration projects

Trackers

Active markdown files that capture ongoing work and action items:

• Auto-categorized by context (business, personal, project, system)

• YAML frontmatter for metadata and workflow control

• Natural language task formatting with AI assistance

AI Inference

Intelligent routing that understands your workflow:

• Context detection from existing tracker patterns

• Item type classification (action, review, reference, someday/maybe)

• Automatic prioritization and dependency discovery

🚀 Getting Started

Prerequisites

• Node.js 18+

• GitHub Copilot or compatible AI assistant

• OpenAI API key for AI inference

• Existing Churn system directory structure

Installation

Configuration

1. Create :

   { "collectionsPath": "/path/to/your/Collections", "trackingPath": "/path/to/your/tracking", "crossrefPath": "/path/to/crossref.json", "aiProvider": "openai", "aiApiKey": "your-openai-key", "confidenceThreshold": 0.7 }

2. Set up GitHub Copilot (see MCP-SETUP.md for complete guide)

Usage with GitHub Copilot

1. Configure GitHub Copilot with ChurnFlow MCP server:

   { "mcpServers": { "churnflow": { "command": "tsx", "args": ["/path/to/churn-mcp/src/index.ts"], "cwd": "/path/to/churn-mcp" } } }

2. Start the MCP server:

   npm run mcp

3. Use with GitHub Copilot:

   • "Use ChurnFlow to capture 'Need to call parts supplier about carburetor for John Deere restoration'"

   • "What's the status of my ChurnFlow system?"

   • "Show me my available ChurnFlow trackers"

Database Features (Optional)

Database setup enables advanced features while maintaining full file-based compatibility:

Database Features:

• 🔍 Full-text search across all captures

• 📊 Analytics dashboard with statistics

• 🧠 AI learning that improves over time

• 📋 Review prioritization for ADHD workflows

File-Only Mode: ChurnFlow works perfectly without database setup - all captures save to markdown files as usual.

CLI Usage (Alternative)

🎯 Core Features

🤖 AI Assistant Integration (v0.3.0)

• GitHub Copilot Ready: Full MCP server with three tools (capture, status, list_trackers)

• Multi-AI Support: Works with any MCP-compatible AI assistant

• Natural Conversations: "Use ChurnFlow to capture..." or "What should I work on?"

• Cross-Interface Sync: Seamless between AI assistants and CLI

🧐 Smart Capture

• Multi-Item Processing: Single brain dump generates multiple routed items

• Context Inference: AI routes to appropriate trackers automatically

• Natural Language: "Working on Gibson website, need to call client, update docs"

• Confidence-Based Routing: High confidence items placed directly, low confidence flagged for review

• Complete Review Integration: Low-confidence items properly routed through ReviewManager for human oversight

✨ Perfect Formatting (v0.2.2)

• ISO Date Standards: Consistent 2025-09-16 and 2025-09-16 14:30 formats

• Priority Indicators: Visual emojis (🚨 ⏫ 🔼 🔻) for quick scanning

• Section Placement: Items go exactly where they belong in tracker files

• ADHD-Friendly: Clean, consistent output reduces cognitive load

🔧 Production Ready

• 176+ Comprehensive Tests: Full test coverage across all components including database

• Dual Storage System: Redundant file + database storage with graceful fallback

• Error Handling: Graceful degradation ensures no thoughts are lost

• Emergency Capture: Always saves input even when systems fail

• Clean Architecture: Database setup separated from capture operations

• Multi-Item Support: Doug welder example processes complex scenarios

🏢 About GSC Dev

ChurnFlow is developed by Gibson Service Company, LLC - Development Division (GSC Dev), the R&D arm of a multi-division business focused on bringing joy-driven solutions to market.

Other GSC Divisions:

• Gibson Service Company: Small engine repair & vintage tractor restoration (gibsonsvc.com)

• GSC AI Consulting: AI-powered workflows for small businesses

• Project-55: Building financial independence through passion-driven entrepreneurship

🤝 Contributing

We welcome contributions from the ADHD and neurodivergent community! Please see our Contributing Guidelines for details.

📋 Roadmap

✅ Completed

• v0.2.1: Multi-item capture with cross-tracker routing

• v0.2.2: Complete formatting consistency and perfect section placement

• v0.3.0: MCP server integration with GitHub Copilot support

• v0.3.1: Review Process system foundation

• v0.3.2: Complete ADHD dashboard & task management system

• v0.3.3: Complete task editing and lifecycle management

• v0.3.4: Review system integration - complete capture → review → action workflow

• v0.4.0: Complete SQLite database integration with FTS, analytics, and AI learning

🏁 Next (v0.4.3)

• Capture input refinements: Enhanced AI processing and edge cases

• Priority detection: Improved priority inference from natural language

• Multi-item enhancement: Improved multi-item capture from complex inputs

• Edge case handling: Better handling of ambiguous or unusual inputs

• Confidence scoring: Refined confidence algorithms for routing decisions

🚀 Future Releases

• v0.4.4: Context-aware dashboard views with database backing

• v0.4.5: Inferred due dates with AI learning patterns

• v0.4.6: Enhanced MCP server with database-powered tools

• v0.5.0: Voice memo capture system with database integration

• v0.6.0: Smart sync system with database analytics

• v0.7.0+: Advanced AI features, mobile app, community features

🏆 Roadmap Principles

💪 Database-First Architecture

• All future features leverage SQLite database foundation

• Dual storage (files + database) ensures backwards compatibility

• Database analytics and learning enhance every feature

• Clean migrations enable safe schema evolution

🧠 ADHD-Focused Development

• MVP refinements based on real usage patterns

• Incremental improvements over major rewrites

• Database insights drive UX optimizations

• Maintain zero-friction capture workflow

🚀 Proven Velocity

• v0.4.0 database integration completed in focused sessions

• Each version builds incrementally on solid foundation

• Database infrastructure enables rapid feature development

• Clear, bounded objectives for each release

📄 License

MIT License - see LICENSE for details.

🆘 Support

• 📚 Documentation

• 🐛 Issues

• 💬 Discussions

Built with ❤️ for the ADHD community by someone who gets it.