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.
After adding an MCP server with specialized code refactoring tools (extract_function, rename_variable, inline_function), You notice the agent still uses basic text manipulation via Write and Bash sed commands for refactoring tasks. The MCP server is connected and healthy.
Examining the configuration, you find each MCP tool has a minimal description like
"extract_function: Extracts a function from code."
What's the most effective way to improve adoption of the MCP refactoring tools?

• A. Accept this as expected behavior since simpler tools like sed are more predictable than specialized refactoring tools.
• B. Implement a request classifier that detects refactoring intent and automatically routes those requests to the MCP server before the agent processes them.
• C. Remove the Write tool from the agent's configuration for refactoring sessions so it must use the MCP tools for code modifications.
• D. Enhance the MCP tool descriptions to explain when each tool is preferable to text manipulation and clarify expected inputs and outputs.

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

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 asked Claude to write a data migration script, but the initial output doesn't correctly handle records with null values in required fields. What's the most effective way to iterate toward a working solution?

• A. Provide a test case with example input containing null values and the expected output, then ask Claude to fix it.
• B. Describe the null value problem in detail and ask Claude to regenerate the entire script with improved edge case handling.
• C. Manually edit the generated code to fix the null handling, then continue working with Claude on other parts.
• D. Add "think harder about edge cases" to your prompt and request a complete rewrite of the migration logic.

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.
Your agent has spent 25 minutes exploring a game engine's rendering subsystem -reading shader code, buffer management, and frame synchronization logic. An engineer now asks it to understand how the physics engine integrates with rendering for collision debug overlays. You notice recent responses reference "typical rendering patterns" rather than the specific VulkanPipeline and FrameGraph classes it discovered earlier.
What's the most effective approach?

• A. Continue in the current context with more targeted prompts referencing the specific classes by name.
• B. Spawn a sub-agent to explore physics independently, then manually synthesize its findings with the rendering knowledge accumulated in the main conversation.
• C. Summarize key rendering findings, then spawn a sub-agent for physics exploration with that summary in its initial context.
• D. Use /clear to reset context completely, then start fresh with physics exploration using file paths from the project's CLAUDE.md.