Your Claude agent has too many tools, and many of them have overlapping functionality. The agent often picks an inappropriate tool when several could plausibly handle a request.
How would you address the tool selection problem?

• A. Add more tools to cover every variation of the requests the agent handles, on the grounds that more tools give the agent more accurate options to choose from.
• B. Restructure the tool set by consolidating overlapping tools, removing unused tools, and clarifying tool descriptions so each tool has a distinct purpose.
• C. Remove all tools and rely on the agent's general capability instead, with the application losing the workflows that previously relied on tools.
• D. Add detailed examples to each tool's description so the agent can match incoming requests to the right tool by example, treating the examples as the team's selection mechanism.

You are designing an agent that handles a complex claim-processing workflow. Each claim moves through fact extraction, eligibility evaluation, and a decision step. The three subtasks have distinct success criteria, and some claims require iteration between fact extraction and eligibility evaluation before a decision can be reached.
Which agent pattern would you apply?

• A. A graph-based pattern that lets the agent move between subtasks based on the state of each claim, with each subtask evaluated against its own criteria.
• B. A single tool-use loop pattern that gives one agent access to all the tools needed for fact extraction, eligibility evaluation, and decision-making.
• C. A linear chain pattern that processes every claim through fact extraction, then eligibility evaluation, then decision, with no return paths between subtasks.
• D. A streaming pattern that emits partial decisions as the agent processes each claim, refining the output until a final decision emerges from the stream.

You are starting a new Claude application and have a small set of well-labeled examples that demonstrate the desired output format. You want to use these examples to guide Claude's behavior.
How would you guide the application's behavior?

• A. Embed the examples in a database and retrieve them at runtime as reference material for the team.
• B. Use multi-shot prompting by including the labeled examples in the prompt so Claude can match the desired output format on each request.
• C. Train a custom model on the labeled examples before deployment so that the application does not need to include the examples in any prompt during operation.
• D. Use zero-shot prompting and rely on the model's general capability to produce the desired output format consistently across all incoming requests.

Your team is debating how to manage the prompts used in your Claude application. Some prompts are checked into the code repository, some live in a separate configuration file, and some are constructed inline at runtime. The result is inconsistent, and a recent prompt change went out without code review.
What steps would you take?

• A. Establish a single source of truth for prompts but keep change review optional, allowing developers to update prompts directly when changes are urgent.
• B. Establish a single source of truth for prompts, version-control them alongside code, and require code review for prompt changes.
• C. Move all prompts to inline runtime construction so the team can update them quickly through a streamlined process outside the standard code review workflow.
• D. Move all prompts out of version control to a separate spreadsheet that team members can edit freely as the application evolves over time.

A teammate has asked you to explain when a Skill would be the right choice over an MCP server. The teammate is unsure how the two differ in practice when both can be reused across teams.
How would you explain the distinction?

• A. A Skill and an MCP server are equivalent extension mechanisms that the team can use interchangeably for any reusable capability that needs to be accessible across teams.
• B. A Skill is preferable for cross-team reuse because it loads more efficiently than an MCP server during normal operation in the team's typical multi-team workloads.
• C. A Skill is the older mechanism and an MCP server is the newer one, so the team should prefer an MCP server for any reusable capability that the team builds going forward.
• D. A Skill bundles prompts, scripts, and data into a package the model loads as a unit while an MCP server exposes resources, tools, and prompts through a standard client interface.