Products Sales Analysis
L3
PlaywrightShopping Admin
Generate comprehensive sales performance reports by extracting product metrics, analyzing revenue trends, identifying top performers, evaluating inventory turnover, and creating actionable insights.
Created by Fanqing Meng
2025-08-17
Data ExtractionComparative AnalysisContent Submission
Model Ranking
Click on the dots to view the trajectory of each task run
Model | Run Results | Pass@4 | Pass^4 | Avg Time | Avg Turns | Input Tokens | Output Tokens | Total Tokens |
---|---|---|---|---|---|---|---|---|
claude-4-1-opus | 0 /1 | - | - | 115.0s | 8.0 | 71,720 | 1,140 | 72,860 |
claude-4-sonnet | 0 /4 | 138.7s | 8.5 | 291,568 | 1,153 | 292,721 | ||
deepseek-chat | 0 /4 | 175.9s | 13.3 | 152,628 | 1,218 | 153,846 | ||
gemini-2-5-pro | 0 /4 | 132.8s | 8.8 | 2,269,537 | 1,962 | 2,271,499 | ||
gpt-5 | 0 /4 | 134.3s | 9.0 | 367,260 | 3,804 | 371,064 | ||
grok-4 | 0 /4 | 22.1s | 0.8 | - | - | - | ||
k2 | 0 /4 | 130.2s | 8.5 | 72,917 | 493 | 73,410 | ||
o3 | 0 /4 | 104.1s | 9.0 | 293,430 | 1,997 | 295,427 | ||
qwen-3-coder | 0 /4 | 91.4s | 9.3 | 321,643 | 388 | 322,030 |
Task State
Instruction
Verify
Python
import asyncio
import sys
import re
import os
import json
from pathlib import Path
def get_model_response():
"""
Get the model's response from the MCP_MESSAGES environment variable.
Returns the last assistant message text.
"""
messages_path = os.getenv("MCP_MESSAGES")
print(f"MCP_MESSAGES: {messages_path}")
if not messages_path:
print("Warning: MCP_MESSAGES environment variable not set", file=sys.stderr)
return None
try:
with open(messages_path, "r") as f:
messages = json.load(f)
# Find the last assistant message
for message in reversed(messages):
if (
message.get("role") == "assistant"
and message.get("status") == "completed"
):
content = message.get("content", [])
for item in content:
if item.get("type") == "output_text":
return item.get("text", "")
print("Warning: No assistant response found in messages", file=sys.stderr)
return None
except Exception as e:
print(f"Error reading messages file: {str(e)}", file=sys.stderr)
return None
def parse_answer_format(text):
"""
Parse the <answer>...</answer> format from the agent's output.
Returns a dictionary with the parsed values.
"""
if not text:
print("Error: No text provided to parse", file=sys.stderr)
return None
# Look for <answer>...</answer> pattern
match = re.search(r"<answer>(.*?)</answer>", text, re.IGNORECASE | re.DOTALL)
if not match:
print("Error: No <answer>...</answer> tags found in response", file=sys.stderr)
return None
answer_content = match.group(1).strip()
if not answer_content:
print("Error: Empty answer content", file=sys.stderr)
return None
# Parse each line
result = {}
lines = [line.strip() for line in answer_content.split("\n") if line.strip()]
if len(lines) != 10:
print(f"Error: Expected 10 lines in answer, got {len(lines)}", file=sys.stderr)
print(f"Lines found: {lines}", file=sys.stderr)
return None
# Expected keys for validation
expected_keys = [
"YogaProducts", "WH11Price", "ZeroQuantityProducts", "LowestProduct",
"QuestLumaflexQuantity", "DashboardRevenue", "SarahMillerEmail",
"TotalCustomers", "PendingOrders", "GraceNguyenOrderID"
]
for line in lines:
if "|" not in line:
print(f"Error: Line missing '|' separator: {line}", file=sys.stderr)
return None
parts = line.split("|", 1)
if len(parts) != 2:
print(f"Error: Invalid line format: {line}", file=sys.stderr)
return None
key, value = parts[0].strip(), parts[1].strip()
if not key or not value:
print(f"Error: Empty key or value in line: {line}", file=sys.stderr)
return None
result[key] = value
# Validate all expected keys are present
missing_keys = set(expected_keys) - set(result.keys())
if missing_keys:
print(f"Error: Missing required keys: {missing_keys}", file=sys.stderr)
return None
return result
def load_expected_answer(label_path):
"""
Load the expected answer from label.txt file.
Returns a dictionary with the expected values.
"""
try:
with open(label_path, "r") as f:
lines = f.read().strip().split("\n")
expected = {}
for line in lines:
if "|" in line:
key, value = line.split("|", 1)
expected[key.strip()] = value.strip()
return expected
except Exception as e:
print(f"Error reading label file: {str(e)}", file=sys.stderr)
return None
def compare_answers(model_answer, expected_answer):
"""
Compare the model's answer with the expected answer.
Returns True if all key information matches, False otherwise.
"""
if not model_answer or not expected_answer:
return False
# Check each expected key
mismatches = []
for key, expected_value in expected_answer.items():
model_value = model_answer.get(key, "")
# Special handling for different types of values
if key == "LowestProduct":
# Check if product name and quantity match (format: "Product Name:quantity")
if ":" in expected_value and ":" in model_value:
expected_name, expected_qty = expected_value.rsplit(":", 1)
model_name, model_qty = model_value.rsplit(":", 1)
if expected_name != model_name or expected_qty != model_qty:
mismatches.append(
f"{key}: expected '{expected_value}', got '{model_value}'"
)
else:
if expected_value != model_value:
mismatches.append(
f"{key}: expected '{expected_value}', got '{model_value}'"
)
elif key in ["WH11Price", "DashboardRevenue"]:
# For price/amount fields, normalize format
expected_clean = expected_value.replace("$", "").replace(",", "")
model_clean = model_value.replace("$", "").replace(",", "")
if expected_clean != model_clean:
mismatches.append(
f"{key}: expected '{expected_value}', got '{model_value}'"
)
elif key == "SarahMillerEmail":
# Email should match exactly
if model_value.lower() != expected_value.lower():
mismatches.append(
f"{key}: expected '{expected_value}', got '{model_value}'"
)
else:
# Exact match for other fields
if model_value != expected_value:
mismatches.append(
f"{key}: expected '{expected_value}', got '{model_value}'"
)
if mismatches:
print("\n=== Answer Comparison Mismatches ===", file=sys.stderr)
for mismatch in mismatches:
print(f"✗ {mismatch}", file=sys.stderr)
return False
print("\n=== Answer Comparison ===", file=sys.stderr)
print("✓ All key information matches the expected answer", file=sys.stderr)
return True
async def verify() -> bool:
"""
Verifies that the products and sales analysis task has been completed correctly.
First checks the model's answer against the expected label,
then optionally verifies the actual state in the Magento Admin.
"""
# Get the label file path
label_path = Path(__file__).parent / "label.txt"
# Load expected answer
expected_answer = load_expected_answer(label_path)
if not expected_answer:
print("Error: Could not load expected answer from label.txt", file=sys.stderr)
return False
# Get model's response from MCP_MESSAGES
model_response = get_model_response()
if model_response:
print("Found model response, parsing answer format...", file=sys.stderr)
model_answer = parse_answer_format(model_response)
if model_answer:
print("\n=== Model Answer Parsed ===", file=sys.stderr)
for key, value in model_answer.items():
print(f"{key}: {value}", file=sys.stderr)
# Compare answers
answer_match = compare_answers(model_answer, expected_answer)
if not answer_match:
print("\nModel answer does not match expected answer", file=sys.stderr)
return False
print("\n✓ Model answer matches expected answer", file=sys.stderr)
return True
else:
print(
"Warning: Could not parse answer format from model response",
file=sys.stderr,
)
return False
else:
print("No model response found", file=sys.stderr)
return False
def main():
"""
Executes the verification process and exits with a status code.
"""
result = asyncio.run(verify())
sys.exit(0 if result else 1)
if __name__ == "__main__":
main()