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Caldova planners need answers from the company's supply chain policy instead of the model's training data. Which capability should you add to the agent?
Grounding, by attaching the supply chain policy with a File search tool.
A second model deployment in a different region.
A higher token limit on the model.
You add a draft_capacity_request function to the Caldova assistant. Which statement best describes the declarative nature of this custom tool?
draft_capacity_request
You must write code that explicitly calls each tool function in sequence.
The agent decides when and how to call a tool based on the prompt and the tool's description.
Tools run automatically on a fixed schedule regardless of the prompt.
Caldova exposes material stock through a remote MCP server. What is the main advantage of this approach instead of hardcoding the stock tool?
It removes the need for the agent to use a model.
Tools can be discovered dynamically at runtime, so they can be added or updated centrally without changing agent code.
It guarantees the agent never needs approval to call a tool.
A capacity-request tool is configured with require_approval="always". What does the agent return before submitting the request?
require_approval="always"
An mcp_approval_request that your code responds to with an mcp_approval_response.
mcp_approval_request
mcp_approval_response
A final answer with the tool result already included.
An error that stops the run.
You must answer all questions before checking your work.
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