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 coordinator cannot track which capabilities each subagent has, leading to misrouted tasks.
• C. Choosing from 18 tools instead of 4-5 relevant ones increases decision complexity beyond reliable selection thresholds.
• D. The agents' role descriptions in their system prompts conflict with having access to tools outside that role.

A retail organization observes that Claude occasionally gives different wording when summarizing identical customer reviews. The summaries remain accurate. What is the MOST likely explanation?

• A. Claude is malfunctioning.
• B. The API returned cached responses.
• C. Retrieval failed.
• D. Large language models are probabilistic.

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 is configuring MCP servers in Claude Code. You want to add a shared venue lookup server that all team members should have access to, and you personally want to add an experimental music playlist server that only you are testing. Which configuration approach correctly applies MCP server scopes?

• A. Add both servers to the project-level .mcp.json file
• B. Add both servers to your local ~/.claude.json
• C. Add venue server to ~/.claude.json and playlist server to .mcp.json
• D. Add venue server to .mcp.json and playlist server to ~/.claude.json.

After integrating a local MCP server providing code analysis tools (analyze_dependencies, find_dead_code, calculate _complexity), you verify the server is healthy and tools appear in the tools/list response. However, you observe that the agent consistently uses Grep to search for import statements instead of calling analyze_dependencies -even when users explicitly ask about "code dependencies." Examining tool definitions reveals:
MCP: analyze_ dependencies - "Analyzes dependency graph"
Built-in: Grep - "Search file contents for a pattern using regular
expressions. Returns matching lines with line numbers and surrounding
context."
What's the most effective approach to improve the agent's selection of MCP tools?

• A. Split analyze_dependencies into granular tools ( list_imports, resolve_transitive_deps, detect_circular_deps) so each has a focused purpose less likely to overlap with Grep.
• B. Remove Grep from available tools when the MCP server is connected to eliminate functional overlap.
• C. Expand MCP tool descriptions to detail capabilities and outputs - e.g., "Builds dependency graph showing direct imports, transitive dependencies, and cycles."
• D. Add routing instructions to the system prompt specifying that dependency-related questions should use MCP tools rather than Grep.

Your agent has called lookup_order multiple times while investigating a customer's return requests. Each response includes 40+ fields (items, shipping details, payment info, status history). Tool outputs now represent the majority of the conversation's context. The customer mentions two more orders they want to discuss. What's the most effective approach before making additional lookups?

• A. Proceed with additional lookups without modifying the existing tool output context
• B. Move all tool responses to a vector database with semantic indexing, retrieving relevant portions as the conversation continues
• C. Have the model generate a natural language summary of each order's key details, replacing structured responses with prose descriptions
• D. Extract only the return-relevant fields (items, purchase date, return window, status) from each existing order response, removing verbose details