# Dataset MCP Implementation - Complete ✅

## overview

successfully implemented a clean dataset discovery and context assembly system for the mcp server. agents can now discover available datasets and request specific context on-demand without context flooding.

## what was built

### 1. dataset organization

```
src/prompts/datasets/
  upc.py              # infrastructure availability data
  upc_take_up.py      # modeled subscriber estimates
  upc_forecast.py     # predictive forecasting data
  ontology.py         # ontology of telco entities
```

each dataset file contains:

- `get_dataset_summary()` - brief description for discovery
- `get_db_info()` - complete instructions/schema/examples

### 2. context assembly system

**src/core/context_assembly.py** provides:

- `get_available_datasets()` - auto-discovers dataset files
- `list_datasets()` - formatted list with summaries
- `assemble_context(dataset_names)` - builds full context from selected datasets

### 3. mcp server tools

**server.py** now exposes two new tools:

#### `list_available_datasets()`

shows agent what datasets exist with brief descriptions

#### `assemble_dataset_context(dataset_names: List[str])`

assembles full context for chosen datasets (instructions + schema + examples)

## agent workflow

1. **discovery**: call `list_available_datasets()` to see options
2. **context assembly**: call `assemble_dataset_context(['upc', 'upc_take_up'])` for specific needs
3. **query execution**: use assembled context to write proper sql queries

## key benefits

- **no context flooding**: agent only gets what it needs
- **auto-discovery**: new datasets automatically available
- **lazy loading**: summaries are cheap, full context expensive
- **flexible combinations**: mix and match datasets per query
- **maintainable**: each dataset self-contained

## tested and verified ✅

- all module imports working
- dataset discovery functional
- context assembly working for single/multiple datasets
- error handling for invalid datasets
- mcp server tools properly registered

## data clarity improvements

updated take-up dataset description to clarify these are "modeled subscriber estimates" using "algorithmic distribution of reported isp totals using probability models" - not actual subscriber numbers.

**the system is production ready** 🚀
