CCDV-F Tools and MCP Integration Practice Question
A developer is building a customer-support agent using the Anthropic API with the Claude 3.5 Sonnet model. The agent exposes a `lookup_order` tool that takes an `order_id` string. During testing, a user asks 'Where is my order 88213?' and Claude returns a `tool_use` block with `name: "lookup_order"` and `input: {"order_id": "88213"}`. What must the developer send back to Claude in the next API request so it can produce a natural-language answer?
⚠ Common exam trap
The trap here is assuming tool output can be sent as ordinary user text or a system message instead of a properly paired `tool_result` block.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
A `user` message containing a `tool_result` content block whose `tool_use_id` matches the `tool_use` block and whose `content` holds the lookup result.
After Claude emits a `tool_use` block, the developer must return a user message containing a `tool_result` block that carries the same `tool_use_id`. This pairing is how the model links its request to the returned data. Once the matching result is provided, Claude can generate the final answer about the order. Any other message shape leaves the tool call unsatisfied and prevents a grounded response.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
A new `system` message that contains the order details and instructs Claude to answer the user.
Why it's wrong here
System messages set behavior and context at the top of a conversation; they are not the channel for tool outputs. Injecting order data into a system message mid-conversation is not supported by the Messages API and would not satisfy the `tool_use`/`tool_result` pairing. Claude would still be waiting for the matching `tool_result`, so the turn would stall or error out for this support agent.
- ✓
A `user` message containing a `tool_result` content block whose `tool_use_id` matches the `tool_use` block and whose `content` holds the lookup result.
Why this is correct
The Anthropic Messages API requires that after Claude emits a `tool_use` block, the developer replies with a `user` message containing a `tool_result` block referencing the same `tool_use_id`. This lets Claude bind the returned order data to its earlier request. Only then will the model generate the final natural-language reply about order 88213 using the actual lookup output.
- ✗
An `assistant` message that echoes the `tool_use` block and appends the lookup result inside the same block.
Why it's wrong here
Tool results must not be placed in an `assistant` message. The assistant role is reserved for model turns, and mutating the `tool_use` block would violate the API schema. Claude expects results in a subsequent user message as a distinct `tool_result` block. Echoing the call back as assistant content would likely trigger a validation error and definitely would not give the model the order data it needs.
- ✗
A `user` message containing the raw JSON result of the order lookup, with no `tool_use_id` reference.
Why it's wrong here
Sending the raw result as a plain user message breaks the tool-result contract. Claude expects a `tool_result` content block whose `tool_use_id` matches the earlier `tool_use` block, so it can correlate the data with the exact invocation. Without that ID, Claude cannot reliably associate the output with the call and may treat the data as unrelated conversation text, producing a confused or hallucinated answer in this support scenario.
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JA
Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Anthropic exam blueprint
This CCDV-F practice question is part of Courseiva's free Anthropic certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the CCDV-F exam.