# Completed Work

## 2026-02

### Agent Hints Pattern Implementation (2026-02-06)

Consolidated query execution to follow agent-first principle - all operations now return optimization hints inline. See [spec](../specs/005-agent-hints-pattern.md).

**Changes:**

- `core/engine.py`:
  - Updated `execute_sql()` to always return `QueryExecutionResult` with metrics, suggestions, and warnings
  - Removed `execute_sql_with_metrics()` method (functionality consolidated)
  - Added `entity_name` and `created_by` parameters to `execute_sql()`

- `integrations/mcp/server.py`:
  - Updated `kameleondb_execute_sql` tool to return hints by default
  - Removed `kameleondb_execute_sql_with_metrics` tool (redundant)
  - All MCP queries now include performance metrics and optimization suggestions

- `cli/commands/query.py`:
  - Updated `query run` command to display metrics and hints
  - Added `--entity` flag for better optimization hints
  - Added `--metrics/--no-metrics` flag to control display
  - Updated `query validate` to show warnings

- Tests updated:
  - `test_query_intelligence.py` - Updated to use consolidated `execute_sql()`
  - `test_storage.py` - Updated to access `result.rows` instead of treating result as list

- Documentation updated:
  - README.md - Updated Query Intelligence section
  - CHANGELOG.md - Added unreleased changes
  - spec 002 - Noted consolidation
  - spec 005 - Created comprehensive design document

**Benefits:**

- **Agent-First**: Agents get intelligence without needing special knowledge
- **Simpler API**: One function instead of two
- **Consistent Results**: All queries return same structure
- **Progressive Discovery**: Hints guide agents to better patterns
- **MCP Simplification**: One tool instead of two

**Testing:**

- 141/143 unit tests passing (2 PostgreSQL connection tests skipped)
- All query intelligence tests passing
- All storage tests passing

### CLI Tool Implementation (2026-02-06)

Complete command-line interface built with Typer and Rich. See [spec](../specs/004-cli-tool.md).

**Modules:**

- `cli/context.py` - Database connection management with lazy initialization
- `cli/output.py` - Dual output mode (Rich tables for terminal, JSON for agents)
- `cli/parsing.py` - Field syntax parser (`name:type:modifier`) and JSON/JSONL file readers
- `cli/main.py` - Typer app with global options (`--database`, `--json`, `--echo`)

**Command Groups:**

- **schema** (6 commands):
  - `list` - List all entities with Rich table output
  - `describe` - Entity details with fields and relationships tables
  - `create` - Create entities with inline fields or from JSON file
  - `drop` - Soft delete with confirmation prompt
  - `add-field` - Add fields using `name:type:modifier` syntax
  - `context` - LLM-ready schema context for SQL generation

- **data** (5 commands):
  - `insert` - Insert records from inline JSON, JSON file, or JSONL batch
  - `get` - Retrieve record by ID
  - `update` - Update record with JSON patch
  - `delete` - Soft delete record
  - `list` - List records with pagination

- **query** (2 commands):
  - `run` - Execute validated SQL with read-only mode
  - `validate` - Validate SQL without execution

- **storage** (3 commands):
  - `status` - Show storage mode and query stats
  - `materialize` - Migrate to dedicated storage with progress bar
  - `dematerialize` - Migrate back to shared storage

- **admin** (3 commands):
  - `init` - Initialize database with meta-tables
  - `info` - Database connection and entity statistics
  - `changelog` - Schema audit trail

**Features:**

- JSON file input for both schema and data operations
- JSONL batch insert for bulk data loading
- Rich formatting with colored tables and progress bars
- JSON output mode for agent/script integration
- Environment variable support (`KAMELEONDB_URL`)
- Tab completion support (Typer built-in)
- Audit trail support (`--created-by`, `--reason`)

**Entry Point:**

- `kameleondb` command installed via `pyproject.toml` script

---

## 2026-02

### Hybrid Storage Phase 2 - Dedicated Tables & Migration

Implemented dedicated storage mode with bidirectional migration.

**Storage Layer:**

- `DedicatedTableManager` class in `storage/dedicated.py`
  - `create_dedicated_table()` - DDL generation with typed columns
  - `drop_dedicated_table()` - Table cleanup
  - `add_foreign_key()` / `remove_foreign_key()` - FK constraint management
  - `table_exists()` and `get_row_count()` utilities

- `StorageMigration` class in `storage/migration.py`
  - `migrate_to_dedicated()` - Shared → dedicated with batching
  - `migrate_to_shared()` - Dedicated → shared (reversible)
  - Transaction safety with rollback on error
  - Progress callbacks for monitoring
  - `MigrationResult` dataclass with metrics

**Engine Methods:**

- `materialize_entity()` in `core/engine.py`
  - Creates dedicated table and migrates data
  - Logs to schema changelog
  - Returns migration result with timing

- `dematerialize_entity()` in `core/engine.py`
  - Reverses materialization safely
  - Drops dedicated table after migration

**Documentation:**

- Updated `docs/specs/001-hybrid-storage.md` with Phase 2 details

---

### Query Intelligence Phase 1 - Metrics Foundation

Implemented query metrics tracking for intelligent materialization suggestions.

**Metrics Collection:**

- `QueryMetric` model in `schema/models.py`
  - `kdb_query_metrics` table
  - Tracks execution time, row count, joins, tables accessed

- `MetricsCollector` class in `query/metrics.py`
  - `record_query()` - Save metrics after execution
  - `get_entity_stats()` - Aggregate statistics by entity
  - `cleanup_old_metrics()` - Retention management

**Core Types:**

- `QueryMetrics`, `MaterializationSuggestion`, `QueryExecutionResult`, `MaterializationPolicy` in `core/types.py`
- `EntityStats` with aggregated metrics and suggestions

**Engine Methods:**

- `execute_sql_with_metrics()` in `core/engine.py`
  - Returns results + metrics + suggestions
  - Backward compatible with `execute_sql()`

- `get_entity_stats()` in `core/engine.py`
  - Historical performance analysis
  - Materialization recommendations

**Documentation:**

- Created `docs/specs/002-query-intelligence.md`

---

### Hybrid Storage Foundation (Phase 1)

Implemented relationship metadata system without changing storage behavior.

**Database Schema:**

- `storage_mode` column in `kdb_entity_definitions` (default: 'shared')
- `dedicated_table_name` column in `kdb_entity_definitions`
- `kdb_relationship_definitions` table
- `kdb_junction_tables` table

**Schema Engine:**

- `RelationshipDefinition` and `JunctionTable` models
- `add_relationship()` method with validation
- `remove_relationship()` method (soft delete)
- `list_relationships()` and `get_relationships()` methods
- Foreign key field auto-generation
- Updated `describe()` to include relationships

**MCP Tools:**

- `kameleondb_add_relationship`
- `kameleondb_remove_relationship`
- `kameleondb_list_relationships`

---

### LLM Query Support (Phase 1)

Implemented schema context and SQL validation for agent-generated queries.

**Schema Context:**

- `get_schema_context()` method with entity filtering
- `SchemaContextBuilder` class
- PostgreSQL JSONB and SQLite JSON1 dialect support
- Example queries for each dialect
- Relationship join hints

**Query Validation:**

- `QueryValidator` class
- SQL injection detection (18+ patterns)
- Query type validation (SELECT-only default)
- Table access control
- Warning generation

**SQL Execution:**

- `execute_sql()` method with validation
- Result transformation to JSON

**MCP Tools:**

- `kameleondb_get_schema_context`
- `kameleondb_execute_sql`

