Name: Sports Schedule Analyzer
Author: zjunlp

1. Identify and Load Schedule Data

Primary Source: Look for a spreadsheet containing the schedule. The user may specify a title (e.g., "NHL 2425 Schedule") or you may need to list available spreadsheets to find it.
Confirm Structure: The schedule should contain at minimum: Date, Visitor Team, and Home Team columns. Additional columns (time, scores, status) are acceptable but not required for core analysis.
Load Data: Use the google_sheet-get_sheet_data tool to fetch the raw data. Be prepared for large datasets; the tool may return an "overlong" output saved to a file.

2. Parse and Prepare Data

Execute the Parsing Script: Run the bundled parse_schedule.py script. It will:
1. Load the overlong tool output from the workspace dump directory.
2. Extract the Date, Visitor, and Home fields from each row (skipping the header).

Primary Source: Look for a spreadsheet containing the schedule. The user may specify a title (e.g., "NHL 2425 Schedule") or you may need to list available spreadsheets to find it.
Confirm Structure: The schedule should contain at minimum: Date, Visitor Team, and Home Team columns. Additional columns (time, scores, status) are acceptable but not required for core analysis.
Load Data: Use the google_sheet-get_sheet_data tool to fetch the raw data. Be prepared for large datasets; the tool may return an "overlong" output saved to a file.

Execute the Parsing Script: Run the bundled parse_schedule.py script. It will:
1. Load the overlong tool output from the workspace dump directory.
2. Extract the Date, Visitor, and Home fields from each row (skipping the header).