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: Broken link to label descriptions
content: Hey, I was checking out the dataset and I noticed that the link to the label description is now broken.
involved: [
    {
        "name": "datacard.md",
        "context": "## Descriptions

[Link to label descriptions](https://github.com/selfagency/bs-detector/blob/master/chrome/data/data.json)"
    }
]
</issue>

<hint>
a broken link to label descriptions in the 'datacard.md' under the 'Descriptions' section
</hint>

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

<answer>
The content of the `datacard.md` provides enough information to identify a potential issue regarding the hint provided. Here is the identified issue:

```json
{
    "issue": "Broken Link to Label Descriptions",
    "evidence": "[Link to label descriptions](https://github.com/selfagency/bs-detector/blob/master/chrome/data/data.json)",
    "description": "The link provided in the 'Descriptions' section of the 'datacard.md' file, intended to direct readers to label descriptions, appears to be broken or potentially outdated. This link is crucial for understanding the dataset's labels, and if it is not working, it hinders the ability of users to fully grasp the dataset's structure and intended use. Ensuring this link is functional and directs to the correct resource is essential for dataset usability."
}
```

This assessment is based on the given hint which focuses on identifying a broken link to label descriptions under the 'Descriptions' section of the 'datacard.md' file. The evidence directly quotes the relevant section of the document. The description explains how this issue affects dataset usability and understanding.

There are no further files mentioned for review related to the hint provided, as the issue directly pertains to the contents within `datacard.md`.
</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.
