reading-plus-ai/mcp-server-data-exploration

AI Evaluation Report

Total Score: 3/10

The agent demonstrates a significant limitation in its ability to access and load CSV files from external URLs, particularly from platforms like GitHub and Kaggle. This limitation prevents it from performing any data analysis tasks that require loading datasets from these sources. The agent's strength lies in its ability to execute Python scripts and perform data analysis once the data is loaded into a DataFrame, but this capability is not effectively tested due to the repeated failures in loading the datasets. The agent's performance is hindered by its reliance on local file paths for CSV files, which restricts its usability in scenarios where datasets are hosted online.

• Test case 1

  Score: 6/10

  Using the 'customers-100.csv' dataset from the 'datablist/sample-csv-files' repository on GitHub, perform the operation of loading the CSV file into a DataFrame and calculating the average number of days between the 'Subscription Date' and today's date for all customers. ([github.com](https://github.com/datablist/sample-csv-files?utm_source=openai))

  No valid answer is generated due to invalid input. The CSV file could not be loaded because the URL provided returned a 404 error, indicating that the file is not found. Please check the URL or provide an alternative source for the dataset.

• Test case 2

  Score: 6/10

  Using the 'USA Real Estate Dataset' from Kaggle, perform the operation of loading the CSV file into a DataFrame and calculating the average listing price for properties in California. The dataset can be found at https://www.kaggle.com/datasets/iamsouravbanerjee/usa-real-estate-dataset.

  No valid answer is generated due to invalid input. The failure is due to accessibility issues with the provided URLs, resulting in HTTP Error (404: Not Found and 403: Forbidden). Please download the dataset manually from Kaggle and upload it here for further assistance with the analysis.

• Test case 3

  Score: 6/10

  Using the 'Global Superstore' dataset from Kaggle, perform the operation of loading the CSV file into a DataFrame and calculating the total sales for each country. The dataset can be found at https://www.kaggle.com/datasets/rohitsahoo/global-superstore.

  No valid answer is generated due to invalid input. The response indicates that the URL for the CSV file is not accessible, resulting in an error: 'Error loading CSV: HTTP Error 404: Not Found.' This suggests that the dataset needs to be downloaded manually from Kaggle and provided as a local file path for further analysis.

• Test case 4

  Score: 6/10

  Using the 'Global Superstore' dataset from Kaggle, perform the operation of loading the CSV file into a DataFrame and calculating the total sales for each country. The dataset can be found at https://www.kaggle.com/datasets/rohitsahoo/global-superstore.

  No valid answer is generated due to invalid input. The response indicates that the CSV file path for the 'Global Superstore' dataset is required to proceed with loading the data into a DataFrame and calculating the total sales for each country. Without the file path, the task cannot be completed.

• Test case 5

  Score: 6/10

  Using the 'Online Retail II' dataset from the UCI Machine Learning Repository, perform the operation of loading the CSV file into a DataFrame and calculating the total sales revenue for each country. The dataset can be found at https://archive.ics.uci.edu/ml/datasets/Online+Retail+II.

  No valid answer is generated due to invalid input. The response indicates that the task requires loading a CSV file from a specified path, but the CSV file path for the 'Online Retail II' dataset was not provided. Therefore, the task could not be completed.