# dataset architecture exploration complete

## what was accomplished

### 1. comprehensive codebase analysis ✅

explored the dataset system architecture:
- **discovery mechanism**: `context_assembly.py` auto-discovers `.py` files in datasets directory
- **integration pattern**: datasets expose `get_dataset_summary()` and `get_db_info()` functions
- **mcp tool registration**: `assemble_dataset_context()` tool dynamically includes available datasets in description
- **lazy loading**: summaries always visible, full context loaded on-demand

### 2. readme documentation added ✅

added comprehensive **"adding new datasets"** section to readme:
- clear step-by-step process
- function requirements and patterns  
- key principles (context efficiency, lazy loading, auto-discovery)
- optimization tips for maintainability

### 3. agent prompt template created ✅

created `.dev/dataset_addition_prompt_template.md`:
- structured template for future dataset additions
- best practices for `get_dataset_summary()` (max 2-3 lines)
- `get_db_info()` structure guidelines
- testing checklist and optimization checklist
- example usage with placeholders

### 4. context window optimization analysis ✅

created `.dev/context_optimization_analysis.md`:
- **current dataset analysis**: identified verbosity issues (upc_take_up: 85+ words)
- **optimization recommendations**: reduced summaries by 60-70% while maintaining clarity
- **token estimation**: target 50% reduction in context size per dataset
- **tariffs dataset guidelines**: specific recommendations for streamlined implementation

## key insights discovered

### current architecture strengths
- **auto-discovery**: new datasets automatically appear in tools
- **modular design**: each dataset self-contained
- **flexible combinations**: agents can request multiple datasets

### optimization opportunities  
- **summary verbosity**: some summaries are 3x longer than needed
- **redundant content**: schema details don't belong in always-visible summaries
- **context bloat**: full examples should be more targeted

### for tariffs implementation
- **summary target**: max 20 words focused on pricing/plan data
- **essential tables only**: avoid geographic redundancy with upc dataset
- **streamlined examples**: 3-4 queries max, common use cases
- **focus on differences**: what tariffs data provides that upc doesn't

## recommendations for next steps

1. **implement tariffs dataset** using optimization guidelines
2. **consider refactoring existing datasets** to reduce context bloat (especially upc_take_up)
3. **establish word limits** for new dataset summaries (20-25 words max)
4. **create context monitoring** to track token usage as datasets grow

## files created/modified

- `README.md` - added dataset addition guide
- `.dev/dataset_addition_prompt_template.md` - agent template
- `.dev/context_optimization_analysis.md` - optimization recommendations
- `.dev/progress_reports/dataset_exploration_complete.md` - this summary

the system is now ready for efficient tariffs dataset addition with clear guidelines for maintaining context window efficiency.

