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1. After the web search agent finds 25 sources (120K tokens of raw content), the document analysis agent extracts key insights (15K tokens), and the synthesis agent produces a coherent narrative draft (3K tokens), the coordinator must pass context to the report generation agent for the final output with proper source citations. What context-passing strategy provides the best balance of completeness and efficiency?

A) Pass a condensed summary of all prior stages that preserves the main findings and attributes them to sources by name only.
B) Pass the full accumulated context from all prior agents.
C) Pass only the synthesis draft and have a separate post-processing pipeline match claims to sources and insert citations after the report is generated.
D) Pass the synthesis draft along with a structured source index that maps key claims to their source URLs and relevant excerpts.

2. Your get_portfolio_value tool returns the total value of a user's investment portfolio. You're deciding between returning a structured JSON object with explicit fields versus returning the information as a formatted text string. What is the primary advantage of using structured output with defined fields?

A) Structured JSON consumes significantly fewer tokens than natural language, substantially reducing API costs.
B) Structured JSON is processed deterministically by the model, significantly improving accuracy when extracting values.
C) JSON schemas automatically validate that the underlying API returned correct data before the agent processes it.
D) The agent can reliably extract specific values without parsing free-form text, reducing errors in subsequent operations.

3. Your agent is handling a billing dispute. After calling get_customer and lookup_order, it identifies that the dispute involves a promotional pricing error requiring manager approval - beyond the agent's authorization level. How should the workflow handle this mid-process escalation?

A) Persist the complete conversation and tool response history to a database, then call escalate_to_human with a reference ID.
B) Compile a structured handoff with customer details, order info, and the identified issue before calling escalate_to_human.
C) Attempt the refund with process_refund anyway, escalating only if the system rejects the transaction.
D) Call escalate_to_human passing only the customer's original message.

4. Your document extraction tool uses ML models to extract invoice fields (vendor, amount, date).
The models return confidence scores (0.0-1.0) for each extracted field. In production, you observe: (1) the agent proceeds with low-confidence extractions that are incorrect 23% of the time, and (2) the agent requests unnecessary human review for 31% of extractions that were actually correct. How should you restructure the tool's output?

A) Compute an aggregate extraction_quality score across all fields and return it alongside the extracted values. Include a text summary describing the overall extraction reliability.
B) Return fields organized into verified and needs_verification objects based on confidence thresholds.
C) Return fields with confidence scores, plus a request_review boolean computed using your tested confidence thresholds, along with a review_reasons array explaining which fields triggered review.
D) Return fields with their raw confidence scores and add detailed few-shot examples to your system prompt demonstrating how to interpret different confidence ranges and when to request human review.

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

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