NY Expansion Analysis

L3
ModelContextProtocolPlaywrightShopping Admin

Prepare New York market expansion strategy by analyzing regional demographics, evaluating competitor presence, assessing logistics requirements, and creating detailed market entry plan.

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
claude-opus-4-1
0
/1
--
312.5s
17.0
588,846
2,206
591,052
Claude
claude-sonnet-4
0
/4
203.0s
16.8
845,070
3,030
848,100
Claude
claude-sonnet-4-high
0
/4
163.8s
16.3
528,117
2,718
530,835
Claude
claude-sonnet-4-low
0
/4
121.9s
15.5
459,487
2,767
462,255
DeepSeek
deepseek-chat
0
/4
304.2s
23.8
948,093
2,678
950,771
Gemini
gemini-2-5-flash
0
/4
180.3s
32.5
1,079,891
8,058
1,087,949
Gemini
gemini-2-5-pro
0
/4
78.3s
12.5
300,324
2,663
302,987
Z.ai
glm-4-5
0
/4
179.5s
23.3
935,044
2,776
937,820
OpenAI
gpt-4-1
0
/4
84.5s
14.0
497,294
980
498,274
OpenAI
gpt-4-1-mini
0
/4
87.3s
22.0
337,059
2,933
339,992
OpenAI
gpt-4-1-nano
0
/4
38.9s
12.3
271,456
790
272,246
OpenAI
gpt-5-high
0
/4
1083.9s
22.0
617,825
39,741
657,566
OpenAI
gpt-5-low
0
/4
434.0s
20.8
587,114
19,302
606,416
OpenAI
gpt-5-medium
0
/4
437.2s
20.5
531,742
20,499
552,240
OpenAI
gpt-5-mini-high
0
/4
324.2s
26.3
2,340,017
21,279
2,361,296
OpenAI
gpt-5-mini-low
0
/4
34.7s
8.8
87,151
854
88,005
OpenAI
gpt-5-mini-medium
0
/4
114.1s
17.8
410,935
7,075
418,010
OpenAI
gpt-5-nano-high
0
/4
353.9s
39.5
3,043,006
39,087
3,082,093
OpenAI
gpt-5-nano-low
0
/4
160.3s
17.3
498,827
13,595
512,422
OpenAI
gpt-5-nano-medium
0
/4
190.0s
29.5
1,156,684
19,801
1,176,485
OpenAI
gpt-oss-120b
0
/4
44.1s
9.5
95,085
1,556
96,641
Grok
grok-4
0
/4
176.8s
14.0
378,161
5,723
383,884
Grok
grok-code-fast-1
0
/4
128.2s
19.5
1,347,592
5,195
1,352,786
MoonshotAI
kimi-k2-0711
0
/4
190.3s
21.0
596,223
1,717
597,940
MoonshotAI
kimi-k2-0905
0
/4
191.9s
19.8
554,511
1,665
556,176
OpenAI
o3
0
/4
212.0s
19.8
487,804
9,220
497,024
OpenAI
o4-mini
0
/4
251.6s
11.3
190,247
15,156
205,402
Qwen
qwen-3-coder-plus
0
/4
1061.7s
24.0
5,765,566
2,047
5,767,613
Qwen
qwen-3-max
0
/4
189.6s
24.5
875,346
1,067
876,413

Task State

WebArena
view WebArena environment setup for this task

Instruction

Our company is planning to expand sales operations to New York state and needs a comprehensive analysis of our current sales performance and tax implications. Please help me gather critical data for our expansion feasibility report.

Task Requirements:

  1. Log in with username 'admin' and password 'admin1234'

  2. First, analyze our current sales performance on the dashboard:

    • Check the 'Lifetime Sales' amount displayed
    • In the Bestsellers table, identify which product has lowest price and record its exact name, price, and quantity sold
    • Find if this same product appears in the 'Last Orders' table, and if so, note which customer(s) ordered it, if no, note 'No'
  3. Since we're expanding to New York, we need check tax:

    • Find and record the exact tax rate for New York state
    • Compare it with California's tax rate - record which state has a higher rate
    • Count how many different US states currently have tax configurations
  4. You need to understand our order status of stores processing for the NY market:

    • Filter orders to show only statuses that are 'Visible On Storefront = Yes'
    • Among these visible statuses, identify if exists one has the status code 'processing' (Yes or No),
    • Check if this 'processing' status is set as a 'Default Status' (Yes or No)
  5. Since New York orders might need special handling, check all stores:

    • Note the number of website configured
    • Record the store code for the first Main Website Store
  6. For inventory planning, check the sources of it:

    • Check if the Default Source is currently 'Enabled' or shows as 'Disabled' for Pickup Location
    • Click the 'Edit' link for the Default Source and check if there's a 'State/Province' field (Yes or No)
  7. Finally, return to the Dashboard and examine the revenue metrics:

    • Record the current Revenue amount shown
    • Check if Tax and Shipping amounts are both $0.00 (Yes or No)

Please provide your findings in the following exact format:

Plaintext
<answer>
Lifetime_Sales_Amount|amount
Cheap_Bestseller_Name|name
Second_Bestseller_Price|price
Second_Bestseller_Quantity|quantity
Product_In_Last_Orders|yes_or_no
NY_Tax_Rate|rate
CA_Tax_Rate|rate
Higher_Tax_State|state
Total_States_With_Tax|count
Processing_Visible_Storefront|Yes_or_No
Processing_Default_Status|Yes_or_No
Number_Of_Websites|count
Main_Store_Code|code
Default_Source_Pickup_Status|status
Default_Source_State|state_or_none
Dashboard_Revenue|amount
Tax_Shipping_Zero|yes_or_no
</answer>

Example Output:

Plaintext
<answer>
Lifetime_Sales_Amount|$XX.XX
Cheap_Bestseller_Name|Product Name Here
Second_Bestseller_Price|$XX.XX
Second_Bestseller_Quantity|XX
Product_In_Last_Orders|Yes/No
NY_Tax_Rate|X.XXXX
CA_Tax_Rate|X.XXXX
Higher_Tax_State|XX
Total_States_With_Tax|XX
Processing_Visible_Storefront|Yes/No
Processing_Default_Status|Yes/No
Number_Of_Websites|X
Main_Store_Code|code_here
Default_Source_Pickup_Status|Enabled/Disabled
Default_Source_State|State or None
Dashboard_Revenue|$XX.XX
Tax_Shipping_Zero|Yes/No
</answer>


Verify

*.py
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("ERROR: MCP_MESSAGES environment variable not set", file=sys.stderr)
        return None
    
    # Check if file exists
    if not Path(messages_path).exists():
        print(f"ERROR: Messages file not found at path: {messages_path}", file=sys.stderr)
        return None
    
    try:
        with open(messages_path, 'r') as f:
            content = f.read()
            
        # Check if file is empty
        if not content or content.strip() == '""':
            print("ERROR: Messages file is empty or contains only empty string", file=sys.stderr)
            return None
            
        messages = json.loads(content)
        
        # Check if messages is a list
        if not isinstance(messages, list):
            print(f"ERROR: Messages file should contain a list, got {type(messages).__name__}", file=sys.stderr)
            return None
        
        # Find the last assistant message
        for message in reversed(messages):
            if message.get('role') == 'assistant' and message.get('status') == 'completed':
                content = message.get('content', [])
                if not content:
                    print("WARNING: Assistant message has empty content", file=sys.stderr)
                    continue
                    
                for item in content:
                    if item.get('type') == 'output_text':
                        text = item.get('text', '')
                        if not text:
                            print("WARNING: Output text is empty", file=sys.stderr)
                            continue
                        return text
        
        print("ERROR: No assistant response with output_text found in messages", file=sys.stderr)
        return None
    except json.JSONDecodeError as e:
        print(f"ERROR: Invalid JSON in messages file: {str(e)}", file=sys.stderr)
        return None
    except Exception as e:
        print(f"ERROR: Unexpected 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> tags found in the response", file=sys.stderr)
        print(f"  Response preview: {text[:200]}...", file=sys.stderr)
        return None
    
    answer_content = match.group(1).strip()
    
    if not answer_content:
        print("ERROR: Empty content between <answer> tags", file=sys.stderr)
        return None
    
    # Parse each line
    result = {}
    lines = answer_content.split('\n')
    
    # Expected keys that should be present
    expected_keys = [
        'Lifetime_Sales_Amount', 'Cheap_Bestseller_Name', 'Second_Bestseller_Price',
        'Second_Bestseller_Quantity', 'Product_In_Last_Orders', 'NY_Tax_Rate',
        'CA_Tax_Rate', 'Higher_Tax_State', 'Total_States_With_Tax',
        'Processing_Visible_Storefront', 'Processing_Default_Status',
        'Number_Of_Websites', 'Main_Store_Code', 'Default_Source_Pickup_Status',
        'Default_Source_State', 'Dashboard_Revenue', 'Tax_Shipping_Zero'
    ]
    
    parsed_keys = []
    for line in lines:
        line = line.strip()
        if not line:
            continue
            
        if '|' not in line:
            print(f"ERROR: Line missing pipe separator '|': {line}", file=sys.stderr)
            continue
            
        parts = line.split('|', 1)
        if len(parts) != 2:
            print(f"ERROR: Invalid line format: {line}", file=sys.stderr)
            continue
            
        key, value = parts
        key = key.strip()
        value = value.strip()
        
        if not key:
            print(f"ERROR: Empty key in line: {line}", file=sys.stderr)
            continue
            
        result[key] = value
        parsed_keys.append(key)
    
    # Check for missing expected keys
    missing_keys = set(expected_keys) - set(parsed_keys)
    if missing_keys:
        print(f"ERROR: Missing expected keys: {', '.join(sorted(missing_keys))}", file=sys.stderr)
        
    # Check for unexpected keys
    unexpected_keys = set(parsed_keys) - set(expected_keys)
    if unexpected_keys:
        print(f"WARNING: Unexpected keys found: {', '.join(sorted(unexpected_keys))}", file=sys.stderr)
    
    if not result:
        print("ERROR: No valid key-value pairs parsed from answer", 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 in ['Lifetime_Sales_Amount', 'Second_Bestseller_Price', 'Dashboard_Revenue']:
            # 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 in ['NY_Tax_Rate', 'CA_Tax_Rate']:
            # Tax rates - allow different decimal formats
            expected_clean = expected_value.replace('%', '').strip()
            model_clean = model_value.replace('%', '').strip()
            # Convert to float for comparison
            try:
                if float(expected_clean) != float(model_clean):
                    mismatches.append(f"{key}: expected '{expected_value}', got '{model_value}'")
            except ValueError:
                if expected_clean != model_clean:
                    mismatches.append(f"{key}: expected '{expected_value}', got '{model_value}'")
        
        elif key in ['Product_In_Last_Orders', 'Processing_Visible_Storefront', 'Processing_Default_Status', 'Tax_Shipping_Zero']:
            # Yes/No fields - case insensitive
            if model_value.lower() != expected_value.lower():
                mismatches.append(f"{key}: expected '{expected_value}', got '{model_value}'")
        
        elif key == 'Empty_Rows_Yes_Effect':
            # Allow flexible descriptions for this field
            # Just check if model provided some reasonable description
            if not model_value or len(model_value) < 5:
                mismatches.append(f"{key}: expected meaningful description, got '{model_value}'")
        
        elif key == 'Order_Status_Options':
            # Check if main options are mentioned
            expected_options = set(opt.strip() for opt in expected_value.split(','))
            model_options = set(opt.strip() for opt in model_value.split(','))
            if expected_options != model_options:
                mismatches.append(f"{key}: expected '{expected_value}', got '{model_value}'")
        
        elif key == 'Chart_Disabled_Message':
            # Allow some flexibility in message text
            # Check for key words
            if 'disabled' not in model_value.lower() and 'enable' not in model_value.lower():
                mismatches.append(f"{key}: expected message about chart being disabled, got '{model_value}'")
        
        elif key == 'Default_Source_State':
            # Handle 'None' or empty state
            expected_normalized = expected_value.lower() if expected_value.lower() != 'none' else ''
            model_normalized = model_value.lower() if model_value.lower() != 'none' else ''
            if expected_normalized != model_normalized:
                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 NY expansion 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.
    """
    print("\n=== Starting Verification ===", file=sys.stderr)
    
    # Get the label file path
    label_path = Path(__file__).parent / "label.txt"
    
    # Load expected answer
    print("Loading expected answer from label.txt...", file=sys.stderr)
    expected_answer = load_expected_answer(label_path)
    if not expected_answer:
        print("FATAL ERROR: Could not load expected answer from label.txt", file=sys.stderr)
        return False
    
    print(f"Expected answer loaded with {len(expected_answer)} keys", file=sys.stderr)
    
    # Get model's response from MCP_MESSAGES
    print("\nReading model response from MCP_MESSAGES...", file=sys.stderr)
    model_response = get_model_response()
    
    if not model_response:
        print("FATAL ERROR: No valid model response found", file=sys.stderr)
        return False
    
    print(f"Model response found (length: {len(model_response)} chars)", file=sys.stderr)
    print("\nParsing answer format from model response...", file=sys.stderr)
    
    model_answer = parse_answer_format(model_response)
    
    if not model_answer:
        print("FATAL ERROR: Could not parse answer format from model response", file=sys.stderr)
        return False
    
    print(f"\n=== Model Answer Parsed Successfully ===", file=sys.stderr)
    print(f"Parsed {len(model_answer)} key-value pairs", file=sys.stderr)
    
    for key, value in model_answer.items():
        print(f"  {key}: {value}", file=sys.stderr)
    
    # Compare answers
    print("\n=== Comparing Model Answer with Expected Answer ===", file=sys.stderr)
    answer_match = compare_answers(model_answer, expected_answer)
    
    if not answer_match:
        print("\nFATAL ERROR: Model answer does not match expected answer", file=sys.stderr)
        print("Verification FAILED", file=sys.stderr)
        return False
    
    print("\n✓ Model answer matches expected answer", file=sys.stderr)
    print("Verification PASSED", file=sys.stderr)
    return True

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()