langchain\agents\chat\output_parser.py line 18, IndexError: list index out of range

### System Info

langchain ver: 0.0.202
python: 3.10_3

I've got this error:
```python
Traceback (most recent call last):
  File "C:\Users\catsk\SourceCode\azure_openai_poc\venv\lib\site-packages\langchain\agents\chat\output_parser.py", line 18, in parse
    action = text.split("```")[1]
IndexError: list index out of range
``` 
while setting my agent.type is CHAT_ZERO_SHOT_REACT_DESCRIPTION

At that moment, the text content was:
<details><summary>Details</summary>
<p>

I have found the answer to the question.

Final Answer: Yes, xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx.

</p>
</details> 


### Who can help?

_No response_

### Information

- [ ] The official example notebooks/scripts
- [X] My own modified scripts

### Related Components

- [ ] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [ ] Vector Stores / Retrievers
- [ ] Memory
- [X] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async

### Reproduction

* Reproduce steps:
1. Build any agent including some tools, 
2. set the AgentType as CHAT_ZERO_SHOT_REACT_DESCRIPTION
3. ask the agent doing something

Here is the code:
```python
def local_vector_search(question_str,chat_history, collection_name = hr_collection_name):
    embedding = get_openaiembeddings()
    vectorstore = Chroma( embedding_function=embedding,
                          collection_name=collection_name,
                          persist_directory=root_file_path+persist_db,
                          )

    memory = ConversationBufferMemory(memory_key="chat_history", return_messages=True, ai_prefix = "AI超級助理")

    llm = AzureOpenAI(
        deployment_name = global_deployment_id,
        model_name= global_model_name,
        temperature = 0.0)

    chat_llm = AzureChatOpenAI(
        deployment_name = global_deployment_id,
        model_name= global_model_name,
        temperature = 0.2)

    prompt = PromptTemplate(
        template=get_prompt_template_string(),
        input_variables=["question","chat_history"]
    )
    prompt.format(question=question_str,chat_history=chat_history)
    km_chain = ConversationalRetrievalChain.from_llm(
        llm=chat_llm,
        retriever=vectorstore.as_retriever(),
        memory=memory,
        condense_question_prompt=prompt,
    )
    km_tool = Tool(
        name='Knowledge Base',
        func=km_chain.run,
        description='Use this tool first when you want to answer any issue about our company'
    )

    math_math = LLMMathChain(llm=llm)
    math_tool = Tool(
        name='Calculator',
        func=math_math.run,
        description='Useful for when you need to answer questions about math.'
    )
    tools=[math_tool,km_tool]
    agent=initialize_agent(
        agent=AgentType.CHAT_ZERO_SHOT_REACT_DESCRIPTION,
        tools=tools,
        llm=chat_llm,
        verbose=True,
        memory=memory,
        max_iterations=30,
    )
    #result=km_chain(question_str)
    result=agent(question_str)
    print(result)
    return result["output"]
``` 

### Expected behavior

Give me the final answer