Individual Comments

L3
ModelContextProtocolFilesystemLegal Document

Extract and analyze individual reviewer comments on legal clauses across multiple document versions to understand personal perspectives.

Created by Lingjun Chen
2025-08-15
Data ExtractionCross ReferencingPattern Analysis

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
OpenAI
gpt-5-high
4
/4
398.8s
6.5
199,504
12,577
212,081
OpenAI
gpt-5-low
4
/4
153.2s
6.8
172,826
7,674
180,500
OpenAI
gpt-5-medium
4
/4
191.3s
7.5
231,192
9,371
240,563
OpenAI
gpt-5-mini-high
4
/4
90.1s
8.3
262,989
8,243
271,232
OpenAI
gpt-5-mini-medium
4
/4
96.9s
8.5
264,363
9,062
273,424
OpenAI
o3
4
/4
98.5s
9.5
287,857
4,595
292,452
Gemini
gemini-2-5-pro
3
/4
110.2s
9.3
307,835
9,416
317,251
OpenAI
gpt-5-mini-low
1
/4
20.4s
5.5
97,989
772
98,760
Grok
grok-4
1
/4
147.9s
6.8
218,353
5,580
223,933
Claude
claude-opus-4-1
0
/1
--
156.2s
7.0
193,146
1,875
195,021
Claude
claude-sonnet-4
0
/4
84.9s
6.0
184,560
1,813
186,373
Claude
claude-sonnet-4-high
0
/4
54.3s
6.0
194,362
2,095
196,457
Claude
claude-sonnet-4-low
0
/4
80.0s
6.0
194,028
2,081
196,109
DeepSeek
deepseek-chat
0
/4
165.3s
8.3
320,694
1,948
322,641
Gemini
gemini-2-5-flash
0
/4
108.8s
5.3
96,970
20,924
117,893
Z.ai
glm-4-5
0
/4
114.0s
6.3
187,826
3,800
191,625
OpenAI
gpt-4-1
0
/4
32.5s
5.0
128,381
1,891
130,271
OpenAI
gpt-4-1-mini
0
/4
20.9s
5.8
88,930
826
89,756
OpenAI
gpt-4-1-nano
0
/4
17.5s
5.8
205,786
448
206,233
OpenAI
gpt-5-nano-high
0
/4
302.1s
14.3
793,439
53,983
847,421
OpenAI
gpt-5-nano-low
0
/4
100.3s
11.0
376,121
17,041
393,161
OpenAI
gpt-5-nano-medium
0
/4
143.2s
11.0
376,992
26,939
403,931
OpenAI
gpt-oss-120b
0
/4
24.8s
5.5
61,027
1,473
62,500
Grok
grok-code-fast-1
0
/4
29.9s
7.8
252,919
600
256,602
MoonshotAI
kimi-k2-0711
0
/4
95.3s
7.0
171,845
1,434
173,278
MoonshotAI
kimi-k2-0905
0
/4
304.5s
18.5
1,194,078
3,005
1,197,083
OpenAI
o4-mini
0
/4
395.6s
16.3
729,959
17,596
747,555
Qwen
qwen-3-coder-plus
0
/4
60.2s
7.3
278,017
1,452
279,469
Qwen
qwen-3-max
0
/4
70.5s
7.5
268,135
596
268,731

Task State

Task Initial State Files
Download ZIP package to view the complete file structure
legal_document/ └── legal_files/ ├── Preferred_Stock_Purchase_Agreement_v0.txt ├── Preferred_Stock_Purchase_Agreement_v1.txt ├── Preferred_Stock_Purchase_Agreement_v2.txt ├── Preferred_Stock_Purchase_Agreement_v3.txt ├── Preferred_Stock_Purchase_Agreement_v4.txt ├── Preferred_Stock_Purchase_Agreement_v5.txt ├── Preferred_Stock_Purchase_Agreement_v6.txt ├── Preferred_Stock_Purchase_Agreement_v7.txt ├── Preferred_Stock_Purchase_Agreement_v8.txt ├── Preferred_Stock_Purchase_Agreement_v9.txt └── Preferred_Stock_Purchase_Agreement_v10.txt

Instruction

Please use FileSystem tools to finish the following task:

Overview

The folder "legal_files/" contains all versions (Preferred_Stock_Purchase_Agreement_v0.txt -- Preferred_Stock_Purchase_Agreement_v10.txt) of the Stock Purchase Agreement for a corporate investment project.

There are comments in it, come from four people:

  • Bill Harvey (Company CEO)
  • Michelle Jackson (Investor)
  • David Russel (Company Counsel)
  • Tony Taylor (Investor Counsel)

Between v1 and v9, these four people make comments on the clauses. The comment format is [name:content], where:

  • name is the commenter's name
  • content is the revision note

Special Note: If the name is "All parties", it represents a joint comment from all parties, which counts as one comment but does not count toward any individual's personal comment count.

Task

Your task is to count the number of comments made by Bill Harvey (Company CEO), Michelle Jackson (Investor), David Russel (Company Counsel), and Tony Taylor (Investor Counsel) in clauses 1.1, 1.3, 4.6, 4.16, 6.8, and 6.16 in version 5-8. Please generate individual_comment.csv in the main directory where the first row contains these clauses (1.1, 1.3, 4.6, 4.16, 6.8, 6.16) and the first column contains the four names (Bill Harvey, Michelle Jackson, David Russel, Tony Taylor). Fill in the table with the number of comments for each person and each clause. If there are no comments, write 0.



Verify

*.py
Python
#!/usr/bin/env python3
"""
Verification script for Legal Document Individual Comments Task
"""

import sys
from pathlib import Path
import csv
import os

def get_test_directory() -> Path:
    """Get the test directory from FILESYSTEM_TEST_DIR env var."""
    test_root = os.environ.get("FILESYSTEM_TEST_DIR")
    if not test_root:
        raise ValueError("FILESYSTEM_TEST_DIR environment variable is required")
    return Path(test_root)

def verify_output_file_exists(test_dir: Path) -> bool:
    """Verify that the individual_comment.csv file exists."""
    output_file = test_dir / "individual_comment.csv"
    
    if not output_file.exists():
        print("❌ File 'individual_comment.csv' not found")
        return False
    
    print("✅ Output file 'individual_comment.csv' found")
    return True

def verify_csv_format(test_dir: Path) -> bool:
    """Verify that the CSV file has the correct format."""
    output_file = test_dir / "individual_comment.csv"
    
    try:
        with open(output_file, 'r', newline='', encoding='utf-8') as csvfile:
            reader = csv.reader(csvfile)
            rows = list(reader)
            
            if not rows:
                print("❌ CSV file is empty")
                return False
            
            # Check if there are at least 2 rows (header + data)
            if len(rows) < 2:
                print("❌ CSV file has insufficient rows")
                return False
            
            # Check if header row has correct number of columns
            header = rows[0]
            if len(header) != 7:  # First column (can be anything) + 6 clauses
                print(f"❌ Header row has incorrect number of columns: {len(header)}, expected 7")
                return False
            
            # Check if data rows have correct number of columns
            for i, row in enumerate(rows[1:], 1):
                if len(row) != 7:
                    print(f"❌ Data row {i} has incorrect number of columns: {len(row)}, expected 7")
                    return False
            
            print("✅ CSV format is correct")
            return True
            
    except Exception as e:
        print(f"❌ Error reading CSV file: {e}")
        return False

def verify_csv_content(test_dir: Path) -> bool:
    """Verify that the CSV content matches the expected answer exactly."""
    output_file = test_dir / "individual_comment.csv"
    
    try:
        with open(output_file, 'r', newline='', encoding='utf-8') as csvfile:
            reader = csv.reader(csvfile)
            rows = list(reader)
            
            # Expected data based on answer.csv
            expected_data = {
                "Bill Harvey": ["0", "2", "3", "1", "1", "1"],
                "Michelle Jackson": ["0", "1", "2", "1", "1", "1"],
                "David Russel": ["2", "1", "1", "2", "1", "1"],
                "Tony Taylor": ["2", "0", "1", "2", "1", "1"]
            }
            
            # Expected header columns (excluding first column which can be anything)
            expected_header_columns = ["1.1", "1.3", "4.6", "4.16", "6.8", "6.16"]
            
            # Verify header has correct number of columns
            header = rows[0]
            if len(header) != 7:  # First column + 6 clauses
                print(f"❌ Header row has incorrect number of columns: {len(header)}, expected 7")
                return False
            
            # Check if all expected clause columns are present (allow order to be different)
            # Allow first column to be anything, so we check columns 1-6
            header_clauses = header[1:7]
            missing_clauses = []
            for expected_clause in expected_header_columns:
                if expected_clause not in header_clauses:
                    missing_clauses.append(expected_clause)
            
            if missing_clauses:
                print(f"❌ Missing expected clause columns: {missing_clauses}")
                return False
            
            # Check if there are extra clause columns
            extra_clauses = []
            for clause in header_clauses:
                if clause not in expected_header_columns:
                    extra_clauses.append(clause)
            
            if extra_clauses:
                print(f"❌ Unexpected extra clause columns: {extra_clauses}")
                return False
            
            # Create a mapping from expected clause order to actual column indices
            clause_mapping = {}
            for i, clause in enumerate(header_clauses):
                if clause in expected_header_columns:
                    clause_mapping[clause] = i
            
            # Parse the CSV data into a dictionary with correct column mapping
            csv_data = {}
            for row in rows[1:]:
                if len(row) >= 7:
                    name = row[0]
                    # Map values according to the expected clause order
                    values = []
                    for expected_clause in expected_header_columns:
                        col_index = clause_mapping[expected_clause] + 1  # +1 because we skip first column
                        values.append(row[col_index])
                    csv_data[name] = values
            
            # Check if all expected names are present
            missing_names = []
            for expected_name in expected_data:
                if expected_name not in csv_data:
                    missing_names.append(expected_name)
            
            if missing_names:
                print(f"❌ Missing expected names: {missing_names}")
                return False
            
            # Check if there are extra names
            extra_names = []
            for name in csv_data:
                if name not in expected_data:
                    extra_names.append(name)
            
            if extra_names:
                print(f"❌ Unexpected extra names: {extra_names}")
                return False
            
            # Check values for each person
            for name, expected_values in expected_data.items():
                actual_values = csv_data[name]
                
                if actual_values != expected_values:
                    print(f"❌ Values mismatch for {name}:")
                    print(f"   Expected: {expected_values}")
                    print(f"   Got:      {actual_values}")
                    return False
            
            print("✅ CSV content matches expected answer exactly")
            return True
            
    except Exception as e:
        print(f"❌ Error verifying CSV content: {e}")
        return False

def verify_data_accuracy(test_dir: Path) -> bool:
    """Verify that the data values are accurate (all values are non-negative integers)."""
    output_file = test_dir / "individual_comment.csv"
    
    try:
        with open(output_file, 'r', newline='', encoding='utf-8') as csvfile:
            reader = csv.reader(csvfile)
            rows = list(reader)
            
            # Skip header row
            for i, row in enumerate(rows[1:], 1):
                if len(row) >= 7:
                    name = row[0]
                    values = row[1:7]
                    
                    for j, value in enumerate(values, 1):
                        try:
                            int_val = int(value)
                            if int_val < 0:
                                print(f"❌ Row {i}, column {j}: negative value '{value}' for {name}")
                                return False
                        except ValueError:
                            print(f"❌ Row {i}, column {j}: non-integer value '{value}' for {name}")
                            return False
            
            print("✅ All data values are valid non-negative integers")
            return True
            
    except Exception as e:
        print(f"❌ Error verifying data accuracy: {e}")
        return False

def verify_file_location(test_dir: Path) -> bool:
    """Verify that the file is in the main directory (not in a subdirectory)."""
    output_file = test_dir / "individual_comment.csv"
    
    if output_file.exists():
        print("✅ File is located in the main directory")
        return True
    else:
        print("❌ File is not in the main directory")
        return False

def main():
    """Main verification function."""
    test_dir = get_test_directory()
    print("🔍 Verifying Legal Document Individual Comments Task...")
    
    # Define verification steps
    verification_steps = [
        ("Output File Exists", verify_output_file_exists),
        ("CSV Format", verify_csv_format),
        ("CSV Content", verify_csv_content),
        ("Data Accuracy", verify_data_accuracy),
        ("File Location", verify_file_location),
    ]
    
    # Run all verification steps
    all_passed = True
    for step_name, verify_func in verification_steps:
        print(f"\n--- {step_name} ---")
        if not verify_func(test_dir):
            all_passed = False
    
    # Final result
    print("\n" + "="*50)
    if all_passed:
        print("✅ Legal document individual comments task completed correctly!")
        print("🎉 Task verification: PASS")
        sys.exit(0)
    else:
        print("❌ Task verification: FAIL")
        sys.exit(1)

if __name__ == "__main__":
    main()