Design observability for tool choice, tool arguments, and execution outcomes.

Instruction: Trace routing, policy, execution, and user-visible completion separately. Avoid recording secrets or full private payloads.

Context: Instrument an assistant so a correct API response cannot conceal a wrong tool choice or an incomplete user task.

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I would create a trace for the user task and child spans for routing, argument validation, policy evaluation, approval, execution, and result handling. Each event should carry a correlation ID and the relevant tool and schema versions. Sensitive values would be redacted or replaced with bounded metadata.

I would measure...

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Your preparation path

Work through these questions in order. Read the answer aloud, then explain it in your own words.

1. Start with the foundations

Build the vocabulary and explain the core decisions.

  1. When should a model call a tool instead of answering from context? Free sample
  2. How would you explain MCP to a product engineer in one minute? Free sample
  3. What information should the orchestrator validate before executing a tool call? Free sample

2. Apply it to a real workflow

Practice diagnosis, validation, and everyday tradeoffs.

  1. Why do idempotency and retries matter more with AI-driven tool use? Free sample
  2. How do you keep permission boundaries clear when many tools share overlapping capabilities? Member answer
  3. A user revokes access to a connected app mid-session. How should the AI workflow behave? Member answer

3. Prepare for senior discussions

Explain failure boundaries, recovery, and production choices.

  1. Design a fallback strategy when tools are unavailable, degraded, or unauthorized. Member answer
  2. How do you implement MCP step-up authorization without losing scopes or retrying forever? Member answer
  3. An MCP request resumes after user confirmation. How would you validate its requestState? Member answer

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