**What was implemented**  
- Replaced the previous Qdrant/OpenAI stack with **ChromaDB** for vector storage and **Ollama** for embeddings and LLM.  
- Added the missing packages `langchain-community` and `langchain-ollama` to `requirements.txt`.  
- Built a single‑tool FAQ bot that can be used from a CLI or a tiny FastAPI web interface.  
- The bot uses a Retrieval‑QA chain powered by the Chroma collection and an “CurrentTime” MCP‑tool that is invoked when the user asks about time or date.  

**Why the main parts satisfy the assignment**  
- **ChromaDB + Ollama**:  
  ```python
  from langchain_ollama import Ollama, OllamaEmbeddings
  from langchain.vectorstores import Chroma
  embeddings = OllamaEmbeddings(model=OLLAMA_MODEL)
  llm = Ollama(model=OLLAMA_MODEL)
  client = Client(path=CHROMA_DB_PATH)
  collection = client.get_or_create_collection(name="faq")
  vectorstore = Chroma(collection=collection, embedding=embeddings)
  ```  
  These lines show that the vector store is Chroma and the embeddings/LLM come from Ollama, satisfying the core requirement.  

- **Retrieval‑QA chain**:  
  ```python
  retrieval_chain = RetrievalQA.from_chain_type(
      llm=llm,
      chain_type="stuff",
      retriever=vectorstore.as_retriever(),
      chain_type_kwargs={"prompt": prompt},
  )
  ```  
  The chain uses the Chroma retriever and the Ollama LLM, so answers are generated from the FAQ data stored in Chroma.  

- **MCP‑tool integration**:  
  ```python
  def get_current_time(_input: str) -> str:
      return datetime.now().strftime("%Y-%m-%d %H:%M:%S")
  time_tool = Tool(
      name="CurrentTime",
      description="Returns the current system time. Useful when the user asks about the time or date.",
      func=get_current_time,
  )
  ```  
  The tool is registered and called in `answer_query` when the question contains “time” or “date”.  

- **CLI & web interface**:  
  ```python
  @cli.command()
  @click.argument("question", nargs=-1, required=True)
  def ask(question, init):
      ...
  @app.post("/ask", response_model=AnswerResponse)
  async def ask_endpoint(req: QuestionRequest):
      ...
  ```  
  These provide two simple ways to interact with the bot locally.  

**Short code excerpts**  
- **`src/main.py` – embeddings & vector store**  
  ```python
  embeddings = OllamaEmbeddings(model=OLLAMA_MODEL)
  llm = Ollama(model=OLLAMA_MODEL)
  client = Client(path=CHROMA_DB_PATH)
  collection = client.get_or_create_collection(name="faq")
  vectorstore = Chroma(collection=collection, embedding=embeddings)
  ```  

- **`src/main.py` – RetrievalQA chain**  
  ```python
  retrieval_chain = RetrievalQA.from_chain_type(
      llm=llm,
      chain_type="stuff",
      retriever=vectorstore.as_retriever(),
      chain_type_kwargs={"prompt": prompt},
  )
  ```  

- **`src/main.py` – MCP‑tool**  
  ```python
  def get_current_time(_input: str) -> str:
      return datetime.now().strftime("%Y-%m-%d %H:%M:%S")
  time_tool = Tool(
      name="CurrentTime",
      description="Returns the current system time. Useful when the user asks about the time or date.",
      func=get_current_time,
  )
  ```  

- **`src/main.py` – CLI command**  
  ```python
  @cli.command()
  @click.argument("question", nargs=-1, required=True)
  def ask(question, init):
      ...
  ```  

**Honest limitations**  
- The solution assumes an Ollama server is running locally and reachable; no fallback or error handling for connection failures.  
- The FAQ ingestion is a one‑time upsert; updates to the CSV after startup require re‑running the `ingest_faq` step.  
- No advanced prompt tuning or chain‑type customization beyond the simple “stuff” strategy.  
- The web server is started with `uvicorn` in reload mode; for production use a more robust deployment setup would be needed.  

Overall, the code now meets all constraints: it uses ChromaDB, Ollama embeddings, includes the required packages, and provides a functional FAQ bot with a single MCP‑tool.