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: added note on right-left rendering for Persian texts
content: Persian_idiom_multiple_choice added note on right-left rendering for Persian texts in README.md
involved: [
    [
        {
            "name": "README.md",
            "context": "
**Authors:** Pouya Pezeshkpour (pezeshkp@uci.edu), Daniel Khashabi (danielk@allenai.org), et al.

+
+ **Note:** Due to differences in left-right vs right-left rendering for Persian texts, the `task.json` file may appear malformed depending on the text editor or viewer. 
+
## What is the task trying to measure?

The task is intended to quantify the ability of a model to correctly choose the right candidate from the set of possible choices. We consider three categories of questions in this task: (1) literature (understanding of literary expressions), (2) common-knowledge (encyclopedic knowledge or everyday activities), and (3) math & logic (logical or mathematical problems). Examples from each category of questions are included in [1].
        "
        },
        {
            "name": "task.json",
            "context": "all those persian texts."
        }
    ]
]
</issue>

<hint>
lack of warning on right-left rendering of certain language in README.md affecting presentation in task.json
</hint>

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

<answer>
Based on the analysis of the provided documents and considering the given hint about the lack of warning on right-left rendering of certain language in the README.md affecting presentation in task.json, I have identified the following issues:

1.  **Issue related to presentation of Persian text in `task.json`**
    - **Issue**: "Right-to-left (RTL) text presentation issue"
    - **Evidence**: The `task.json` file contains Persian content, such as: 
    ```
    "input": "«بهترین پاسخ به «خیر، ممنون» چیست؟", 
    "target_scores": {
        "بله، خواهش میکنم": 1,
        ...
    ```
    This snippet indicates the use of Persian, an RTL language, within the dataset. 
    - **Description**: Given that Persian text flows from right to left, the lack of explicit guidance in `README.md` on handling RTL script presentation might lead to improper display or alignment issues when visualizing or using the dataset, especially in environments or applications that do not automatically detect and adjust for RTL languages.

2.  **Issue related to clarification in `README.md`**
    - **Issue**: "Absence of rendering instructions for RTL languages"
    - **Evidence**: The `README.md` content mentions several sources from which the task data is derived but does not provide any instructions on dealing with RTL languages:
    ```
    ## Data source

    This task is self-created. Many common and well-known Persian idiom are available from the websites...
    ```
    - **Description**: The `README.md` file does not specify that the dataset includes content in a right-to-left language (Persian) or offer guidance on how such content should be properly displayed or processed. This absence of a warning or instructions is problematic for contributors and users who might utilize the dataset in environments that require explicit RTL configuration, inevitably affecting the data presentation quality.
</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.
