CellarTracker Data Parser

Table Overview

Eight tables are exported from CellarTracker. Not all will always be available — work with what's present.

Table Priority for Common Tasks

• "What should I drink tonight?" — List (inventory + location) + Availability (maturity) + Notes (past impressions)
• "Evaluate this purchase" — List (redundancy check) + Tag (is it on wishlist?) + Purchase (have I bought this before?)
• "What have I been drinking?" — Consumed (patterns, frequency, notes)
• "Cellar overview / audit" — List (full inventory) + Availability (what's past peak?)
• "How much have I spent?" — Purchase (complete spend history)

Parsing Instructions

All CT export CSVs share these characteristics:

• UTF-8 encoded (converted from windows-1252 by export script)
• Quoted fields, comma-delimited
• First row is always headers
• iWine is the universal join key across all tables
• NV (non-vintage) wines use vintage 1001

Reading CSVs

import csv
with open('List_latest.csv') as f:
    reader = csv.DictReader(f)
    rows = list(reader)

Key Join Pattern

Cross-reference tables using iWine:

# Example: Match inventory to availability/maturity data
list_by_wine = {row['iWine']: row for row in list_rows}
for avail_row in availability_rows:
    cellar_row = list_by_wine.get(avail_row['iWine'])
    if cellar_row:
        # Now have both inventory location AND maturity data

Column Reference

For full column schemas for all 8 tables, see the schema reference file in references/schema.md.

Load the schema reference when you need to:

• Look up specific column names or meanings
• Decode professional reviewer abbreviations (WA, WS, AG, etc.)
• Understand maturity curve fields (Available, Bell, Linear, etc.)
• Parse drinking window source hierarchy

Professional Score Abbreviations

The most commonly relevant scores (full list in schema reference):

Score display rules:

• CT community scores are often decimals (e.g., 88.7777...) — round to 1 decimal
• MY is the user's personal rating — always flag when present
• Empty string = no score available, not zero
• In Availability table, scores also have Web (link) and Sort (numeric) variants

Drinking Window Logic

The Availability table provides the richest maturity data. Key fields:

• BeginConsume / EndConsume: Consensus or personal drinking window (date format: M/D/YYYY or YYYY)
• Source: Where the window comes from — Personal, Community, or a professional reviewer name
• Available: Maturity percentage (0-1 = approaching peak, ~1 = at peak, >1 = past peak)
• Bell / Linear / Early / Late / Fast / TwinPeak / Simple: Different maturity curve models

Maturity interpretation:

• Available < 0.3 — Too young, needs significant time
• Available 0.3-0.7 — Approaching window, can open with decanting
• Available 0.7-1.0 — In window, good to drink
• Available > 1.0 — Past peak or at tail end of window

Window source priority: Personal > Professional reviewer > Community

The List table also has BeginConsume/EndConsume as year integers — use these as quick reference, Availability for detail.

Location & Storage Mapping

The List table's Location and Bin fields map to physical storage. These are example locations — your CellarTracker locations will vary based on your setup:

Common Query Patterns

Bottle Count & Capacity

# Active cellar size (exclude pending)
cellar_count = sum(int(row['Quantity']) for row in list_rows)

Report against your cellar capacity targets when known.

Category Breakdown

# Group by color, region, varietal, etc.
from collections import Counter
by_color = Counter(row['Color'] for row in list_rows)
by_region = Counter(row['Region'] for row in list_rows)
by_varietal = Counter(row['MasterVarietal'] for row in list_rows)

Redundancy Check (for purchase evaluator)

When checking if a new wine would be redundant:

1. Same producer? — Flag
2. Same varietal + region? — Count existing
3. Same broad style (e.g., "California Chardonnay")? — Count existing
4. On wishlist? — Note as positive signal

Drinking Priority

Combine List + Availability to find what should be opened soon:

• Available > 1.0 — Past peak, drink ASAP
• EndConsume year <= current year — Window closing
• Location = Rack — Already in drink-soon storage

Spend Analysis

Purchase table tracks all historical buys:

• Price = cost per bottle in USD
• Remaining vs Quantity shows consumption rate
• StoreName tracks where bottles were sourced
• OrderNumber sometimes has context (e.g., "Gift from Liz")

Wishlist Cross-Reference

Tag table with ListName = *Wishlist:

• WinesNotes often has context on why it was added (e.g., "Reddit QPR white burgundy", "Konstantin Baum best wines of 2025")
• Use during purchase evaluation: if a wine is on the wishlist, it's a positive signal

Integration with Other Skills

wine-purchase-evaluator

When evaluating purchases, this skill provides:

• Redundancy data from inventory (same producer, varietal, region) — use search_cellar MCP tool or List CSV
• Wishlist match from wishlists — use get_wishlist MCP tool or Tag CSV
• Historical pricing from purchase history — use purchase_history MCP tool or Purchase CSV
• Consumption velocity from consumption log — use Consumed CSV

General wine advisory

• "What should I drink with X?" — Query inventory for available bottles matching the pairing need, check maturity
• "Tell me about my cellar" — Full inventory analysis with category breakdown, maturity overview, valuation summary
• "What's past its peak?" — Find wines where Available > 1.0

Output Style

When presenting cellar data:

• Round CT community scores to 1 decimal place
• Format valuations as USD with 2 decimal places
• Use drinking window as year range (e.g., "2025-2030"), not full dates
• Flag wines past peak with a clear indicator
• When listing inventory, include Location for actionability
• Keep tables tight — don't dump all columns, select what's relevant to the query