Your order management system requires tools for three distinct operations: issuing refunds (requires amount and reason), canceling orders (requires reason), and requesting reshipments (requires shipping address). Each operation shares an order_id parameter but has different additional requirements. You notice during testing that with your current unified tool design, the agent frequently omits required parameters or includes irrelevant ones. What design change will most effectively improve parameter accuracy?

• A. Keep one unified tool with a nested operation_details object parameter whose internal structure varies by operation type, documented in the tool description.
• B. Split into three separate tools (issue_refund, cancel_order, request_reshipment), each defining only the parameters required for that specific operation.
• C. Keep one unified tool with all parameters marked optional, but add detailed few-shot examples in the system prompt showing correct parameter combinations for each operation type.
• D. Keep one unified tool but add JSON Schema if-then-else conditionals to enforce that parameters like amount are required only when the operation type is "refund".

You've configured the system so that all four subagents have access to the complete set of
18 tools. During testing, agents frequently call tools outside their specialization - the synthesis agent attempts web searches, and the report generator tries to analyze documents. What is the primary cause of this poor tool selection behavior?

• A. The tool definitions consume too much context window space, leaving insufficient room for task content.
• B. The agents' role descriptions in their system prompts conflict with having access to tools outside that role.
• C. The coordinator cannot track which capabilities each subagent has, leading to misrouted tasks.
• D. Choosing from 18 tools instead of 4-5 relevant ones increases decision complexity beyond reliable selection thresholds.

A customer raises three separate issues during one session: a refund inquiry (turns 1-15), a subscription question (turns 16-30), and a payment method update (turns 31-45). At turn 48, the customer asks "What happened with my refund?" The conversation is approaching context limits.
What strategy best maintains the agent's ability to address all issues throughout the session?

• A. Extract and persist structured issue data (order IDs, amounts, statuses) into a separate context layer.
• B. Implement sliding window context that retains the most recent 30 turns.
• C. Summarize earlier turns into a narrative description, preserving full message history only for the active issue.
• D. Rely on MCP tools to re-fetch relevant information on demand when the customer references earlier issues.

Which practice MOST improves prompt maintainability?

• A. One paragraph containing all instructions.
• B. Minimal punctuation.
• C. Clearly separated sections with headings.
• D. Random ordering.

You are building developer productivity tools using the Claude Agent SDK. The agent helps engineers explore unfamiliar codebases, understand legacy systems, generate boilerplate code, and automate repetitive tasks. It uses the built-in tools (Read, Write, Bash, Grep, Glob) and integrates with Model Context Protocol (MCP) servers.
A developer asks the agent to investigate why a specific API endpoint intermittently returns 500 errors. The codebase has 200+ files and the developer doesn't know which components are involved. The agent must trace the error through routing, middleware, business logic, and database layers. What task decomposition approach would be most effective?

• A. Have the agent first create a comprehensive plan mapping all code paths through the endpoint before beginning any file exploration or code reading.
• B. Run parallel worker agents that simultaneously investigate all four layers, then synthesize their findings to identify where the error originates.
• C. Have the agent dynamically generate investigation subtasks based on what it discovers at each step, adapting its exploration plan as new information about the error path emerges.
• D. Define a fixed sequence of investigation steps upfront - grep for error patterns, then read error handlers, then check database queries, then examine middleware - executing each step regardless of intermediate findings.