Your update_user_profile tool accepts a user_id (required) and an optional fields_to_update object. In testing, Claude frequently omits user_id or passes incorrectly structured data. What is most critical for helping Claude understand what parameter values to provide?

• A. Clear parameter descriptions explaining expected format, such as "user_id: UUID of the user to update (required)"
• B. Verbose parameter names encoding format hints, such as user_id_string_uuid_format
• C. Strict JSON Schema type constraints marking user_id as required and defining fields_to_update as an object type
• D. Detailed error responses explaining why invalid parameter values were rejected

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

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.
Your team has connected a custom MCP server that provides DevOps workflow templates. The server exposes several MCP prompts (such as deploy_checklist and incident_response) in addition to tools. How do these MCP prompts become accessible within Claude Code?

• A. They appear as slash commands (e.g., mcp_servername_deploy_checklist) that you can invoke, with arguments passed after the command name.
• B. They are surfaced as @-mentionable resources alongside files, fetched and attached to your message when referenced.
• C. They are automatically prepended to every conversation as additional system-level context, influencing Claude's behavior throughout the session.
• D. They are added to Claude Code's tool registry alongside the server's tools, invoked automatically by the model when relevant to the task.

In production, final reports frequently contain claims without proper source attribution.
Investigation shows that while the web search and document analysis agents correctly attach citations to their outputs, the synthesis agent loses track of which sources support which conclusions when combining findings. What's the most effective architectural change?

• A. Have the coordinator inject source identifier prefixes into text before each handoff, then parse these prefixes at report generation to reconstruct citations.
• B. Require all subagents to output structured claim-source mappings that the synthesis agent must preserve and merge when combining findings from multiple sources.
• C. Add a verification step where the report generator uses semantic similarity matching against original sources to reconstruct which claims came from which documents.
• D. Maintain complete transcripts of all subagent interactions and add a citation-resolution agent to analyze logs and determine attributions before report generation.