# Getting Started - Planka MCP Server Implementation
## Overview
This project implements a token-optimized MCP server for Planka kanban boards. The implementation is broken down into discrete, self-contained tasks that can be executed by LLM agents independently.
## Documentation Structure
### For Planning & Understanding
1. **`IMPLEMENTATION_PLAN.md`** - High-level design document
- Token optimization strategies
- Tool specifications
- Architecture decisions
- Read this to understand WHY design decisions were made
2. **`TOKEN_EFFICIENCY_ANALYSIS.md`** - Performance analysis
- Detailed token usage projections
- Caching strategies
- Optimization impact calculations
- Read this to understand token savings
3. **`WORKFLOW_OPTIMIZATIONS.md`** - User-specific optimizations
- Based on actual Planka usage patterns
- Cross-list filtering rationale
- Real-world impact analysis
- Read this to understand user requirements
### For Implementation
4. **`IMPLEMENTATION_GUIDE.md`** - Part 1 of task breakdown
- Phases 1-3: Foundation, Infrastructure, Tier 0 tools
- Clear task dependencies
- Validation criteria for each task
- Estimated effort per task
- **Start here for implementation**
5. **`IMPLEMENTATION_GUIDE_PART2.md`** - Part 2 of task breakdown
- Phase 4: Tier 1 tools (core workflow)
- Phase 5: Testing & documentation
- Complete task checklist
- Resume instructions
## Quick Start for Implementers
### If you're starting from scratch:
1. Read `IMPLEMENTATION_GUIDE.md` - Task 1.1: Project Setup
2. Follow tasks sequentially: 1.1 → 1.2 → 1.3 → 2.1 → etc.
3. Each task has:
- Clear objective
- Prerequisites
- Implementation steps
- Validation criteria
- Estimated effort
### If you're resuming work:
1. Check `planka_mcp.py` to see what's already implemented
2. Look for `@mcp.tool` decorators to identify completed tools
3. Find the next incomplete task in the task dependency graph
4. Start from that task
### If you're working in parallel:
Phase 4 tasks (4.1-4.7) can be done in parallel by different agents:
- Task 4.1: planka_list_cards
- Task 4.2: planka_get_card
- Task 4.3: planka_find_and_get_card
- Task 4.4: planka_create_card
- Task 4.5: planka_update_card
- Task 4.6: planka_add_task
- Task 4.7: planka_update_task
All other phases must be completed sequentially.
## Task Dependency Graph
```
Phase 1 (Foundation)
├─ 1.1 Project Setup
├─ 1.2 Environment Configuration [depends: 1.1]
└─ 1.3 API Client [depends: 1.2]
↓
Phase 2 (Infrastructure)
├─ 2.1 Caching System [depends: 1.3]
├─ 2.2 Input Models [depends: 1.3]
└─ 2.3 Response Formatting [depends: 2.2]
↓
Phase 3 (Critical Tool)
└─ 3.1 planka_get_workspace [depends: 2.1, 2.2, 2.3]
↓
Phase 4 (Core Tools) - Can parallelize
├─ 4.1 planka_list_cards [depends: 3.1]
├─ 4.2 planka_get_card [depends: 3.1]
├─ 4.3 planka_find_and_get_card [depends: 4.2]
├─ 4.4 planka_create_card [depends: 3.1]
├─ 4.5 planka_update_card [depends: 4.4]
├─ 4.6 planka_add_task [depends: 4.2]
└─ 4.7 planka_update_task [depends: 4.6]
↓
Phase 5 (Testing & Docs)
├─ 5.1 Manual Testing [depends: all Phase 4]
├─ 5.2 Evaluation Questions [depends: 5.1]
└─ 5.3 README Documentation [depends: 5.1, 5.2]
```
## Total Effort Estimate
- **Phase 1**: 1.1 hours (65 minutes)
- **Phase 2**: 2.4 hours (145 minutes)
- **Phase 3**: 1 hour (60 minutes)
- **Phase 4**: 3.7 hours (220 minutes) - Can parallelize
- **Phase 5**: 3.8 hours (225 minutes)
**Total: ~11.5 hours** (single agent working sequentially)
**Parallelized: ~8 hours** (multiple agents on Phase 4)
## Key Design Principles
### 1. Token Efficiency is Primary Goal
- Single workspace query replaces 3-4 calls (50-66% reduction)
- Smart detail levels (preview/summary/detailed)
- Cross-list queries eliminate multiple calls
- Aggressive caching for 60-70% API call reduction
### 2. User Workflow Optimized
- Single-board usage pattern (categories as lists)
- Heavy task list usage
- "In Progress" label filtering across all lists
- Comment/attachment indicators in preview mode
### 3. LLM-Friendly Implementation
- Each task is self-contained
- Clear validation criteria
- Explicit dependencies
- Resumable at any point
## Common Questions
### Q: Can I implement tools in a different order?
**A:** Only if dependencies are met. Check the dependency graph. Phase 4 tools can be done in any order or in parallel.
### Q: What if I don't understand the Planka API structure?
**A:** The implementation steps include placeholders with comments like "adjust based on actual API response format". Test with the MCP Inspector and adjust code based on real API responses.
### Q: How do I test my implementation?
**A:** Each task has a "Validation" section with specific commands. Use the MCP Inspector for interactive testing:
```bash
npx @modelcontextprotocol/inspector python planka_mcp.py
```
### Q: What if a task takes longer than estimated?
**A:** Estimates are guidelines. Some tasks may take longer depending on API discovery. The critical path is understanding the Planka API response structure.
### Q: Can I skip testing (Phase 5)?
**A:** No. Testing validates that all tools work with the actual Planka API. Evaluation questions demonstrate the server's capabilities.
### Q: What if the Planka API changes?
**A:** The design is flexible. Adjust the API client and response formatting utilities. The tool interfaces remain stable.
## Success Criteria
The implementation is complete when:
### Core Functionality
- [ ] All 9 tools implemented (1 Tier 0, 7 Tier 1, caching system)
- [ ] Server starts without errors
- [ ] All tools can be called via MCP Inspector
- [ ] Tools return properly formatted responses
### Token Efficiency
- [ ] planka_get_workspace returns complete structure in one call
- [ ] Caching system works (verify with repeated calls)
- [ ] Detail levels reduce tokens (preview < summary < detailed)
- [ ] Cross-list queries work (list_id=None)
- [ ] Label filtering works (label_filter parameter)
### Testing
- [ ] All tools tested manually
- [ ] Error handling verified
- [ ] 10 evaluation questions created and verified
- [ ] README documentation complete
### Performance Targets
- [ ] Workspace cache hit rate: 90%+ in conversations
- [ ] Token reduction: 50-60% vs naive implementation
- [ ] API call reduction: 60-70% due to caching
## Next Steps
1. **For first-time implementation:**
- Open `IMPLEMENTATION_GUIDE.md`
- Start with Task 1.1: Project Setup
- Follow tasks sequentially
2. **For continuation:**
- Check `planka_mcp.py` for completed tasks
- Find next incomplete task in dependency graph
- Resume from that task
3. **For testing:**
- Complete all Phase 1-4 tasks first
- Follow Phase 5 testing procedures
- Create evaluation questions based on real data
## Support
If you encounter issues:
1. **API Structure Unknown:**
- Use MCP Inspector to examine actual API responses
- Adjust code to match real structure
- Update comments for future implementers
2. **Tool Not Working:**
- Check validation criteria in task specification
- Test with MCP Inspector
- Review error messages for guidance
3. **Caching Issues:**
- Verify cache methods are called
- Check cache TTLs are appropriate
- Add logging to verify cache hits/misses
4. **Token Usage Higher Than Expected:**
- Verify detail levels are being used
- Check if caching is working
- Ensure cross-list queries are implemented
- Review response formatting
## Files Overview
| File | Purpose | When to Read |
|------|---------|--------------|
| `IMPLEMENTATION_PLAN.md` | Design decisions & specifications | Before starting implementation |
| `TOKEN_EFFICIENCY_ANALYSIS.md` | Performance analysis | To understand optimizations |
| `WORKFLOW_OPTIMIZATIONS.md` | User-specific features | To understand user requirements |
| `IMPLEMENTATION_GUIDE.md` | Task breakdown Part 1 (Phases 1-3) | During implementation |
| `IMPLEMENTATION_GUIDE_PART2.md` | Task breakdown Part 2 (Phases 4-5) | During implementation |
| `GETTING_STARTED.md` | This file - overview & navigation | First thing to read |
---
**Ready to start? Open `IMPLEMENTATION_GUIDE.md` and begin with Task 1.1: Project Setup!**
