You are required to act as an answer evaluator. Given an issue context, the hint disclosed to the agent and the answer from the agent, 
you should rate the performance of the agent into three levels: "failed", "partially", and "success". The rating rules are as follows:

<rules>
1. You will be given a list of <metrics>, for each metric, you should rate in [0,1] for the agent based on the metric criteria, and then multiply the rating by the weight of the metric.
2. If the sum of the ratings is less than 0.45, then the agent is rated as "failed"; if the sum of the ratings is greater than or equal to 0.45 and less than 0.85, then the agent is rated as "partially"; if the sum of the ratings is greater than or equal to 0.85, then the agent is rated as "success".
3. **<text>** means the text is important and should be paid attention to.
4. ****<text>**** means the text is the most important and should be paid attention to.
</rules>

The <metrics> are as follows:
{
    "m1": {
        "criteria": "Precise Contextual Evidence:
            1. The agent must accurately identify and focus on the specific issue mentioned in the context. This involves a close examination of the exact evidence given and determining whether it aligns with the content described in the issue and the involved files.
            2. Always ask yourself: Have the agent provided correct and detailed context evidence to support its finding of issues? If the agent just gives some general description without specifically pointing out where the issues occur, you should give it a low rate.
            3. Once the agent has correctly spotted **** all the issues in <issue> and provided accurate context evidence ****, it should be given a ****full score (1.0) even if it includes other unrelated issues/examples ****"
            4. If the agent has only spotted part of the issues with the relevant context in <issue>, then you should give a medium rate.
            5. The expression in the answer of the agent might not directly pinpoint the issue, but when its answer implies the existence of the <issue> and has provided correct evidence context, then it should be given a high rate for m1.
            6. For issues about something missing and having no clear location information, even if there is context in <issue> involved files, it's ok for the agent to only give an issue description without pointing out where the issue occurs in detail."
         "weight": 0.8,
        "range": [0, 1]
    },
    "m2": {
        "criteria": "Detailed Issue Analysis: 
            1. The agent must provide a detailed analysis of the issue, showing an understanding of how this specific issue could impact the overall task or dataset as human evaluators do.
            2. This metric stresses the importance of not just identifying that there is an issue but also understanding and explaining its implications in detail, rather than simply repeating the information in hint.",
        "weight": 0.15,
        "range": [0, 1]
    },
    "m3": {
        "criteria": "Relevance of Reasoning: 
            1. The agent’s reasoning should directly relate to the specific issue mentioned, highlighting the potential consequences or impacts.
            2. This metric ensures that the agent’s logical reasoning directly applies to the problem at hand, rather than being a generic statement.",
        "weight": 0.05,
        "range": [0, 1]
    }
}
</metrics>

--------------------------------

Now let’s begin:

<issue>
title: Dataset source missing
content: The data source for this dataset is missing from readme.
involved: [
    {
        "name": "readme.md",
        "context": "This file contains the meta info about this dataset, but it does not indicate the source for data. "
    }
]
</issue>

<hint>
missing information in a markdown documentation file
</hint>

-------------------- Below is the answer from the agent. Ensure you don’t take the information above as the agent’s answer!

<answer>
It seems there was a technical hiccup while attempting to access the file content. Let's try again to open and read the markdown (readme.md) file for any missing information based on the hint provided.It seems there was a mix-up with the file identification. The content expected to be markdown documentation (readme.md) appears to be part of the CSV file. Let's correct this by identifying the proper markdown documentation file, which should have been the other file uploaded alongside this.

Let's proceed to read the other uploaded file correctly as the markdown documentation to locate the potential issues according to the hint provided.Upon reviewing the markdown documentation content, the following issue has been identified:

```json
{
    "issue": "Missing file structure description in README",
    "evidence": "This dataset is designed for practicing classification tasks, specifically predicting whether a student will pass or fail a course based on various academic and demographic factors. It contains 40,000 records of students, with attributes including study habits, attendance rates, previous grades, and more.",
    "description": "The README lacks a detailed description of the dataset's file structure, such as the file format (e.g., CSV), the names and types of the columns, or the explanation of coded values if any exist. This information is crucial for users to understand how to use the dataset correctly for their analysis."
}
```

This issue is based on the expectation set by the hint, suggesting a focus on missing information in the markdown documentation. A comprehensive readme file should include a detailed file structure or schema description, which is absent in the provided documentation.
</answer>

-------------------- 

response below, 
1. After your analysis, remember to give a **"decision: [failed/partially/success]"** for me to extract it using REGEX.
2. Don't use Code Interpreter!; Use your ability to analyze the text.  ** Pay attention to your calculations and make sure they are correct. **
3. There could be multiple issues described in <issue> part. You should start by thinking clearly about how many issues exist in <issue> and list them out, then you should compare them with the answer from the agent.
4. you should focus on whether the agent has spotted the issue in <issue> rather than caring about whether the agent includes unrelated examples not present in the context.
