CCDV-F Tools and MCP Integration Practice Question

Answer analysis

Option-by-option breakdown

For each option: why learners choose it and why it is or isn't the right answer here.

• ✓

  Set 'tool_choice' to type 'tool' with a specific name.

  Why this is correct

  By explicitly setting the tool_choice to a specific tool, you are instructing Claude that its next action must be to call that tool. This typically suppresses the generation of conversational preamble, ensuring that the response starts immediately with the tool_use block, which is ideal for programmatic integrations.

• ✗

  Use the 'stop_sequences' parameter to stop at the first period.

  Why it's wrong here

  Stop sequences are used to terminate generation when a specific string is found. This would not prevent the model from starting a sentence; it would only cut the sentence off early. It doesn't guarantee that a tool call will be made or that text will be omitted entirely before the call.

• ✗

  Set 'max_tokens' to a very low value like 10.

  Why it's wrong here

  Setting a very low token limit might cut off the tool call itself, as tool_use blocks often require many tokens for IDs and parameters. This is an unreliable way to suppress text and will likely result in malformed JSON or incomplete tool requests that the client cannot process.

• ✗

  Set the 'system' prompt to 'Do not speak'.

  Why it's wrong here

  While a system prompt can influence behavior, it is less reliable than the 'tool_choice' API parameter. The model might still include brief text or fail to use the tool correctly. API-level enforcement via tool_choice is the architecturally correct way to ensure specific tool-calling behavior in the Anthropic ecosystem.