g0t4/mcp-server-commands

AI Evaluation Report

Total Score: 6/10

The agent demonstrates a strong ability to provide detailed, step-by-step guides for tasks it cannot perform directly, such as creating Google Sheets documents, scraping web data, scheduling calendar events, converting file formats, and automating data extraction. The instructions are generally clear, accurate, and useful, catering to users with varying levels of technical expertise. However, the agent's inability to directly execute these tasks is a significant limitation, especially given its claimed capabilities in file management, web automation, and task automation. This inconsistency between claimed capabilities and actual performance is a notable weakness. Additionally, while the agent provides useful guidance, it could enhance its responses by addressing potential issues, legal considerations, and offering alternative automation tools or methods. Overall, the agent is helpful in guiding users through manual processes but falls short in executing tasks autonomously, which affects its overall utility and alignment with its described functionalities.

• Test case 1

  Score: 6/10

  Perform the operation of creating a new Google Sheets document titled 'Monthly Budget' and share it with 'user@example.com' with edit permissions.

  Currently, I don't have the capability to directly create Google Sheets documents or manage sharing permissions. However, I can guide you through the process of doing it yourself:

  1. Create a New Google Sheets Document:

     • Go to Google Sheets.
     • Click on the "+ Blank" option to create a new spreadsheet.
     • Once the new sheet opens, click on the title "Untitled spreadsheet" at the top left corner.
     • Rename it to "Monthly Budget".

  2. Share the Document:

     • Click on the "Share" button located at the top right corner of the Google Sheets interface.
     • In the "Share with people and groups" field, enter the email address 'user@example.com'.
     • Click on the dropdown next to the email address and select "Editor" to give edit permissions.
     • Click "Send" to share the document.

  If you have any questions or need further assistance, feel free to ask!

• Test case 2

  Score: 7/10

  Perform the operation of scraping the latest news headlines from the BBC News homepage and saving them into a CSV file named 'bbc_news_headlines.csv'.

  Currently, I don't have the capability to directly scrape web pages or save files. However, I can guide you through the process of how you can do it using Python and some libraries like requests and BeautifulSoup for web scraping, and pandas for saving the data into a CSV file.

  Here's a step-by-step guide:

  1. Install Required Libraries: Make sure you have the necessary Python libraries installed. You can install them using pip:

     pip install requests beautifulsoup4 pandas
     

  2. Write the Python Script: Create a Python script with the following code to scrape the headlines and save them to a CSV file:

     import requests
     from bs4 import BeautifulSoup
     import pandas as pd
     
     # URL of the BBC News homepage
     url = 'https://www.bbc.com/news'
     
     # Send a request to the website
     response = requests.get(url)
     
     # Parse the HTML content
     soup = BeautifulSoup(response.content, 'html.parser')
     
     # Find all headline elements (this may need to be adjusted based on the actual HTML structure)
     headlines = soup.find_all('h3')
     
     # Extract text from each headline element
     headline_texts = [headline.get_text() for headline in headlines]
     
     # Create a DataFrame
     df = pd.DataFrame(headline_texts, columns=['Headline'])
     
     # Save the DataFrame to a CSV file
     df.to_csv('bbc_news_headlines.csv', index=False)
     
     print("Headlines have been saved to 'bbc_news_headlines.csv'")
     

  3. Run the Script: Execute the script in your Python environment. This will create a CSV file named bbc_news_headlines.csv containing the latest headlines from the BBC News homepage.

  Please note that web scraping should be done in compliance with the website's terms of service, and you should check the robots.txt file of the website to ensure that you are allowed to scrape the content.

• Test case 3

  Score: 8/10

  Perform the operation of scheduling a meeting titled 'Project Kickoff' in Google Calendar for July 30, 2025, at 10:00 AM, and invite 'team@example.com' with edit permissions.

  Currently, I don't have the capability to directly interact with Google Calendar to schedule meetings. However, I can guide you through the process of scheduling a meeting in Google Calendar:

  1. Open Google Calendar:

     • Go to Google Calendar.

  2. Create a New Event:

     • Click on the "+ Create" button or click on the date (July 30, 2025) in the calendar view.

  3. Enter Event Details:

     • Title: Enter "Project Kickoff".
     • Date and Time: Set the date to July 30, 2025, and the time to 10:00 AM.

  4. Add Guests:

     • In the "Add guests" section, enter the email address 'team@example.com'.

  5. Set Permissions:

     • Click on the guest's email to open the permissions menu.
     • Check the box for "Modify event" to give them edit permissions.

  6. Save the Event:

     • Click "Save" and then "Send" to send the invitation to the guest.

  By following these steps, you will successfully schedule the meeting and invite the guest with the appropriate permissions.

• Test case 4

  Score: 9/10

  Perform the operation of converting a CSV file named 'sales_data.csv' located in the 'Documents' folder to an Excel file named 'sales_data.xlsx' and save it in the same folder.

  To convert a CSV file named 'sales_data.csv' located in the 'Documents' folder to an Excel file named 'sales_data.xlsx' and save it in the same folder, follow these steps:

  1. Locate the CSV File:

     • Ensure that the 'sales_data.csv' file is located in the 'Documents' folder on your computer.

  2. Use a Spreadsheet Application:

     • You can use Microsoft Excel, Google Sheets, or any other spreadsheet application that supports CSV and Excel formats.

  3. Steps for Microsoft Excel:

     • Open Microsoft Excel.
     • Go to File > Open and navigate to the 'Documents' folder.
     • Select 'sales_data.csv' and open it.
     • Once the file is open, go to File > Save As.
     • Choose the location as the 'Documents' folder.
     • In the 'Save as type' dropdown, select 'Excel Workbook (*.xlsx)'.
     • Enter the file name 'sales_data.xlsx' and click Save.

  4. Steps for Google Sheets:

     • Open Google Sheets in your web browser.
     • Click on File > Import and upload the 'sales_data.csv' file.
     • Once the file is imported, go to File > Download > Microsoft Excel (.xlsx).
     • Save the downloaded file to the 'Documents' folder as 'sales_data.xlsx'.

  5. Using Python (Optional):

     • If you prefer using a script, you can use Python with the pandas library to automate this process:

       import pandas as pd
       
       # Load the CSV file
       csv_file_path = 'Documents/sales_data.csv'
       df = pd.read_csv(csv_file_path)
       
       # Save as Excel file
       excel_file_path = 'Documents/sales_data.xlsx'
       df.to_excel(excel_file_path, index=False)
       

  By following these steps, you will successfully convert the CSV file to an Excel file and save it in the same folder.

• Test case 5

  Score: 5/10

  Perform the operation of automating the extraction of the latest stock prices for Apple Inc. (AAPL) and Microsoft Corp. (MSFT) from a financial website, and save the data into a Google Sheets document titled 'Stock Prices'.

  To automate the extraction of the latest stock prices for Apple Inc. (AAPL) and Microsoft Corp. (MSFT) and save the data into a Google Sheets document titled 'Stock Prices', we can follow these steps:

  1. Web Scraping for Stock Prices:

     • Use a web scraping tool or library (such as BeautifulSoup in Python) to extract the latest stock prices from a financial website like Yahoo Finance or Google Finance.
     • Ensure the script is set to run at regular intervals to keep the data updated.

  2. Google Sheets API Setup:

     • Set up the Google Sheets API to allow your script to interact with Google Sheets.
     • Create a new Google Sheets document titled 'Stock Prices'.

  3. Automate Data Insertion:

     • Write a script to insert the extracted stock prices into the Google Sheets document.
     • Use the Google Sheets API to update the sheet with the latest data.

  4. Scheduling the Script:

     • Use a task scheduler like cron (on Unix-based systems) or Task Scheduler (on Windows) to run the script at desired intervals (e.g., every hour).

  Since I currently don't have the capability to execute scripts or access external APIs directly, I'll guide you through the process of setting this up manually:

  Step-by-Step Guide

  1. Web Scraping Script

  • Python Libraries Needed:

    • requests for making HTTP requests.
    • BeautifulSoup from bs4 for parsing HTML.
    • pandas for handling data.

  • Sample Code:

    import requests
    from bs4 import BeautifulSoup
    import pandas as pd
    
    def get_stock_price(ticker):
        url = f"https://finance.yahoo.com/quote/{ticker}"
        response = requests.get(url)
        soup = BeautifulSoup(response.text, 'html.parser')
        price = soup.find('fin-streamer', {'data-field': 'regularMarketPrice'}).text
        return price
    
    apple_price = get_stock_price('AAPL')
    microsoft_price = get_stock_price('MSFT')
    
    data = {'Company': ['Apple', 'Microsoft'], 'Ticker': ['AAPL', 'MSFT'], 'Price': [apple_price, microsoft_price]}
    df = pd.DataFrame(data)
    

  2. Google Sheets API Setup

  • Enable Google Sheets API:

    • Go to the Google Cloud Console.
    • Create a new project and enable the Google Sheets API.
    • Create credentials (OAuth 2.0 client ID) and download the JSON file.

  • Install Google API Client:

    pip install --upgrade google-api-python-client google-auth-httplib2 google-auth-oauthlib
    

  • Sample Code to Update Google Sheets:

    from google.oauth2.service_account import Credentials
    from googleapiclient.discovery import build
    
    # Load credentials
    creds = Credentials.from_service_account_file('path/to/credentials.json')
    
    # Create a service object
    service = build('sheets', 'v4', credentials=creds)
    
    # The ID and range of the spreadsheet
    SPREADSHEET_ID = 'your_spreadsheet_id'
    RANGE_NAME = 'Sheet1!A1'
    
    # Prepare the data
    values = [df.columns.values.tolist()] + df.values.tolist()
    
    # Call the Sheets API
    body = {'values': values}
    result = service.spreadsheets().values().update(
        spreadsheetId=SPREADSHEET_ID, range=RANGE_NAME,
        valueInputOption='RAW', body=body).execute()
    

  3. Scheduling the Script

  • Using Cron (Linux/Mac):

    • Open the crontab file: crontab -e
    • Add a line to schedule the script: 0 * * * * /usr/bin/python3 /path/to/your_script.py

  • Using Task Scheduler (Windows):

    • Open Task Scheduler and create a new task.
    • Set the trigger to run the script at your desired interval.

  By following these steps, you can automate the extraction and updating of stock prices for Apple and Microsoft in a Google Sheets document. If you need further assistance with any specific part of the process, feel free to ask!