# Research Phase 0: MCP Platform Validation
## Overview
This document outlines the critical research phase that **must be completed before** implementing the Universal Memory MCP system. Based on feedback from PR #28, we need to validate our core assumptions about MCP support across target platforms.
## ⚠️ CRITICAL ASSUMPTIONS TO VALIDATE
Our implementation plan assumes:
1. **ChatGPT supports MCP protocol** - **UNVERIFIED**
2. **Cursor supports MCP protocol** - **UNVERIFIED**
3. **Windsurf supports MCP protocol** - **UNVERIFIED**
4. **Export formats are standardizable** - **UNVERIFIED**
5. **Performance scales with multi-platform data** - **UNVERIFIED**
## Research Tasks (20 tasks, ~40-60 hours)
### 🔍 **0.1 MCP Protocol Support Verification** (Critical - 16 hours)
**0.1.1 ChatGPT MCP Support Research** (4 hours)
- Research OpenAI's official MCP documentation
- Check OpenAI developer forums for MCP discussions
- Test ChatGPT with existing MCP servers (if possible)
- Document current status and roadmap
**0.1.2 Cursor MCP Support Research** (4 hours)
- Research Cursor's MCP implementation status
- Check Cursor documentation and GitHub repos
- Test Cursor with simple MCP server
- Contact Cursor team if needed
**0.1.3 Windsurf MCP Support Research** (4 hours)
- Research Windsurf's MCP compatibility plans
- Check Windsurf documentation and community
- Test integration possibilities
- Document findings and limitations
**0.1.4 MCP Compatibility Matrix** (4 hours)
- Create comprehensive comparison table
- Document which features are supported where
- Identify workarounds for unsupported platforms
- **GO/NO-GO decision point**
### 📊 **0.2 Export Format Analysis** (12 hours)
**0.2.1 ChatGPT Export Analysis** (3 hours)
- Export sample ChatGPT conversations
- Analyze JSON structure and metadata
- Identify unique fields and challenges
- Document conversion requirements
**0.2.2 Cursor Export Analysis** (3 hours)
- Export sample Cursor sessions
- Analyze format and structure
- Compare with Claude format
- Document standardization challenges
**0.2.3 Windsurf Export Analysis** (3 hours)
- Export sample Windsurf conversations (if available)
- Analyze format differences
- Identify missing metadata
- Document limitations
**0.2.4 Format Standardization Plan** (3 hours)
- Design universal conversation schema
- Map platform-specific fields to standard format
- Identify lossy vs lossless conversions
- Create format migration strategy
### ⚡ **0.3 Performance Validation** (8 hours)
**0.3.1 Large Dataset Benchmarking** (2 hours)
- Generate 10,000+ test conversations
- Benchmark current search performance
- Identify scaling bottlenecks
- Document baseline metrics
**0.3.2 Multi-Platform Metadata Testing** (2 hours)
- Add platform metadata to test conversations
- Test search performance impact
- Measure storage overhead
- Analyze query complexity scaling
**0.3.3 Storage Scaling Analysis** (2 hours)
- Test with different storage patterns
- Measure disk usage growth
- Analyze index performance degradation
- Document storage optimization needs
**0.3.4 Performance Requirements Documentation** (2 hours)
- Define acceptable performance thresholds
- Create performance test suite
- Document optimization strategies
- Set performance gates for implementation
### 🔧 **0.4 Schema Versioning Strategy** (8 hours)
**0.4.1 Conversation Format Versioning** (2 hours)
- Design schema version system
- Plan forward/backward compatibility
- Create version migration framework
- Document versioning strategy
**0.4.2 Migration Strategy Planning** (2 hours)
- Design data migration workflows
- Plan rollback procedures
- Create migration validation tools
- Document migration safety procedures
**0.4.3 Schema Validation Framework** (2 hours)
- Create JSON schema validation
- Build format compliance testing
- Design error handling for invalid data
- Create validation reporting tools
**0.4.4 Compatibility Documentation** (2 hours)
- Document supported format versions
- Create compatibility matrix
- Plan deprecation timeline
- Create migration guides
### 🚀 **0.5 Proof of Concept - ChatGPT Integration** (16 hours)
**0.5.1 ChatGPT Export Processing** (4 hours)
- Export real ChatGPT conversations
- Parse JSON structure automatically
- Extract metadata and content
- Document conversion challenges
**0.5.2 Basic Importer Prototype** (4 hours)
- Create minimal ChatGPT → Universal format converter
- Implement metadata mapping
- Test with sample data
- Validate data integrity
**0.5.3 MCP Integration Test** (4 hours)
- Set up MCP connection (if ChatGPT supports it)
- Test bidirectional communication
- Verify tool registration
- Document integration challenges
**0.5.4 Feasibility Assessment** (4 hours)
- Document what works vs what doesn't
- Assess effort required for each platform
- Create go/no-go recommendations
- Update implementation plan based on findings
## Success Criteria
### ✅ **Phase 0 Complete When:**
1. **MCP Support Status** - Documented for all target platforms
2. **Export Format Analysis** - Real samples analyzed for each platform
3. **Performance Baseline** - Established with large datasets
4. **Schema Strategy** - Versioning and migration plan complete
5. **Proof of Concept** - At least one additional platform working
6. **Go/No-Go Decision** - Clear recommendation for each platform
### 🚫 **Potential Outcomes:**
**Best Case:** All platforms support MCP → Full universal implementation
**Realistic Case:** Some platforms need bridges → Hybrid approach
**Worst Case:** No additional MCP support → Claude-only with export/import
## Risk Mitigation
### **High Risk: No MCP Support**
- **Fallback Plan:** Create MCP bridge/proxy servers
- **Alternative:** Focus on export/import workflows
- **Mitigation:** Build modular architecture
### **Medium Risk: Format Incompatibility**
- **Fallback Plan:** Create lossy conversion with warnings
- **Alternative:** Platform-specific storage modes
- **Mitigation:** Design flexible schema system
### **Low Risk: Performance Issues**
- **Fallback Plan:** Optimize storage and indexing
- **Alternative:** Implement caching strategies
- **Mitigation:** Set performance gates early
## Timeline
- **Week 1-2:** MCP Support Research (0.1)
- **Week 3:** Export Format Analysis (0.2)
- **Week 4:** Performance & Schema Planning (0.3, 0.4)
- **Week 5-6:** ChatGPT Proof of Concept (0.5)
- **Week 7:** Assessment and Planning
**Total:** ~6-7 weeks of research before implementation begins
## Next Steps
1. **Assign Research Tasks** - Who will research each platform?
2. **Set Up Test Environment** - Prepare for MCP testing
3. **Create Sample Data** - Export conversations from each platform
4. **Schedule Check-ins** - Weekly progress reviews
5. **Prepare Decision Framework** - Criteria for go/no-go decisions
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**⚠️ IMPORTANT:** No implementation work should begin until Phase 0 research is complete and validates our assumptions. This research phase will save significant time and effort by ensuring we build something that actually works across platforms.
