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.
An engineer submits two requests:
- Request A: "Rename the getUserData function to fetchUserProfile
everywhere it's used."
- Request B: "Improve error handling throughout the data processing
module - add try/catch blocks, meaningful error messages, and ensure
failures don't silently corrupt data."
For which request does specifying an explicit multi-phase workflow (such as analyze -> propose
-> implement with review) most improve outcome quality?

• A. Both requests benefit equally
• B. Neither request benefits significantly
• C. Request B, the error handling task
• D. Request A, the function rename task

When implementing your lookup_order MCP tool, the backend sometimes returns errors (e.g.,
"Order not found" or temporary database failures). What is the correct pattern for communicating these errors back to the agent?

• A. Return the error message in the tool result content with the isError flag set to true
• B. Throw an exception from the tool handler so the agent framework can catch and log it
• C. Return a success response with a "status" field indicating the error type
• D. Log the error server-side and return an empty result to avoid confusing the model

Your send_notification tool calls third-party messaging APIs. When these services time out during delivery, you cannot determine whether the message was actually sent. Currently, the tool returns is_error: true with a generic "Notification failed" message for all timeouts. Production monitoring reveals agents automatically retry these failures, frequently causing users to receive duplicate notifications. How should you modify the error response?

• A. Return is_error: true with a message communicating uncertainty: "Timeout - status unknown.
  Message may have been sent. Avoid retry."
• B. Return is_error: true with a message encouraging retry: "Delivery service temporarily unavailable.
  Please retry the notification."
• C. Return is_error: true with the original message content echoed back.
• D. Return is_error: true with a structured field retry_safe: true for timeouts, distinguishing them from permanent failures that should not be retried.

You are using Claude Code to accelerate software development. Your team uses it for code generation, refactoring, debugging, and documentation. You need to integrate it into your development workflow with custom slash commands, CLAUDE.md configurations, and understand when to use plan mode vs direct execution.
You've documented API error handling conventions in a CLAUDE.md file at your project root, specifying that endpoint handlers should use a custom ApiError class. After several sessions, you notice Claude Code sometimes follows these conventions and sometimes uses generic try/catch blocks with string messages. The inconsistency appears random across different coding sessions. What's the most efficient first diagnostic step?

• A. Create path-specific rules in .claude/rules/handlers.md with YAML frontmatter scoping the error handling instructions to your API handler files.
• B. Search for conflicting instructions in ~/.claude/CLauDe.md or ~/.claude/rules/ that might override your project conventions.
• C. Run /memory to check which memory files are loaded and verify your CLAUDE.md is included.
• D. Add a more detailed code examples to your CLAUDE.md showing the exact ApiError usage pattern for different endpoint types.