# Quickstart: Direct Database Access for Scripts

This guide shows how to use the XER MCP Server's persistent database feature to query schedule data directly from scripts.

## Overview

The XER MCP Server can save parsed schedule data to a SQLite file, allowing your scripts to query the data directly without going through MCP tools. This is ideal for:

- Large-scale data analysis
- Custom reporting
- Integration with other tools
- Reducing LLM token usage for data-heavy operations

## Loading to a Persistent Database

### Option 1: Auto-generated Path

Use an empty string for `db_path` to automatically create a database file alongside the XER file:

```
Use the load_xer tool with file_path="/path/to/schedule.xer" and db_path=""
```

**Response**:
```json
{
  "success": true,
  "project": {
    "proj_id": "P001",
    "proj_short_name": "Construction Phase 1"
  },
  "activity_count": 4440,
  "database": {
    "db_path": "/path/to/schedule.sqlite",
    "is_persistent": true,
    "source_file": "/path/to/schedule.xer",
    "loaded_at": "2026-01-08T14:30:00",
    "schema": {
      "version": "0.2.0",
      "tables": [
        {
          "name": "activities",
          "columns": [
            {"name": "task_id", "type": "TEXT", "nullable": false},
            {"name": "task_code", "type": "TEXT", "nullable": false},
            {"name": "task_name", "type": "TEXT", "nullable": false},
            ...
          ],
          "row_count": 4440
        },
        ...
      ]
    }
  }
}
```

### Option 2: Specify Custom Path

```
Use the load_xer tool with file_path="/path/to/schedule.xer" and db_path="/data/my-schedule.db"
```

### Option 3: In-Memory Only (Default)

Omit `db_path` to use the original in-memory behavior:

```
Use the load_xer tool with file_path="/path/to/schedule.xer"
```

## Querying the Database Directly

Once you have the database path, use any SQLite client to query the data.

### Python Example

```python
import sqlite3

# Use the db_path from load_xer response
db_path = "/path/to/schedule.sqlite"

conn = sqlite3.connect(db_path)
conn.row_factory = sqlite3.Row

# Query all milestones
cursor = conn.execute("""
    SELECT task_code, task_name, target_start_date, milestone_type
    FROM activities
    WHERE task_type IN ('TT_Mile', 'TT_FinMile')
    ORDER BY target_start_date
""")

for row in cursor:
    print(f"{row['task_code']}: {row['task_name']} ({row['milestone_type']})")

conn.close()
```

### SQL Examples

**Find critical path activities:**
```sql
SELECT task_code, task_name, target_start_date, target_end_date
FROM activities
WHERE driving_path_flag = 1
ORDER BY target_start_date;
```

**Get activity count by WBS:**
```sql
SELECT w.wbs_name, COUNT(*) as activity_count
FROM activities a
JOIN wbs w ON a.wbs_id = w.wbs_id
GROUP BY w.wbs_id
ORDER BY activity_count DESC;
```

**Find activities with predecessors:**
```sql
SELECT a.task_code, a.task_name,
       COUNT(r.pred_task_id) as predecessor_count
FROM activities a
LEFT JOIN relationships r ON a.task_id = r.task_id
GROUP BY a.task_id
HAVING predecessor_count > 0
ORDER BY predecessor_count DESC;
```

**Export milestones to CSV (using sqlite3 CLI):**
```bash
sqlite3 -header -csv schedule.sqlite \
  "SELECT task_code, task_name, target_start_date, milestone_type
   FROM activities
   WHERE task_type IN ('TT_Mile', 'TT_FinMile')" > milestones.csv
```

## Getting Database Info Without Reloading

If you need the database path and schema later:

```
Use the get_database_info tool
```

**Response**:
```json
{
  "database": {
    "db_path": "/path/to/schedule.sqlite",
    "is_persistent": true,
    "source_file": "/path/to/schedule.xer",
    "loaded_at": "2026-01-08T14:30:00",
    "schema": { ... }
  }
}
```

## Database Schema Reference

| Table | Description | Key Columns |
|-------|-------------|-------------|
| `projects` | Project metadata | proj_id, proj_short_name, plan_start_date, plan_end_date |
| `activities` | Tasks and milestones | task_id, task_code, task_name, task_type, target_start_date, target_end_date |
| `relationships` | Predecessor/successor links | task_pred_id, task_id, pred_task_id, pred_type, lag_hr_cnt |
| `wbs` | Work breakdown structure | wbs_id, wbs_name, parent_wbs_id |
| `calendars` | Calendar definitions | clndr_id, clndr_name, day_hr_cnt |

## Best Practices

1. **Use persistent database for large schedules**: Schedules with 1000+ activities benefit from direct SQL queries

2. **Close connections when done**: Always close your database connections to avoid lock issues

3. **Read-only access recommended**: The database is designed for reading; modifications may cause inconsistencies with MCP tools

4. **Re-load when XER changes**: If the source XER file is updated, reload to refresh the database

5. **Check schema version**: The schema version in the response indicates compatibility with your queries

## Error Handling

| Error Code | Meaning | Solution |
|------------|---------|----------|
| NO_FILE_LOADED | No XER file has been loaded | Call load_xer first |
| FILE_NOT_WRITABLE | Cannot write database file | Check directory permissions |
| DISK_FULL | Insufficient disk space | Free up disk space |
| DATABASE_ERROR | General database error | Check error message for details |