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. Add routing instructions to the system prompt specifying that dependency-related questions should use MCP tools rather than Grep.
• C. Expand MCP tool descriptions to detail capabilities and outputs - e.g., "Builds dependency graph showing direct imports, transitive dependencies, and cycles."
• D. Remove Grep from available tools when the MCP server is connected to eliminate functional overlap.

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Anthropic's tool use documentation states: "Write instructive error messages. Instead of generic errors like 'failed', include what went wrong and what Claude should try next." A billing dispute agent uses lookup_order, which catches all exceptions and returns a tool_result with is_error:
true and the message "Tool execution failed". Monitoring shows two failure modes: the agent retries the identical call until hitting the turn limit, or it immediately calls escalate_to_human without trying alternative tools. Which change follows the documented recommendation and gives Claude the information it needs to select the correct recovery action for each error type?

• A. Implement retry logic with exponential backoff inside each tool implementation so transient errors are resolved transparently within the tool before any failure result is surfaced to Claude in the agentic loop.
• B. Return error-type-specific messages with is_error: true, e.g., "Order not found-try get_customer to search by phone" for data errors and "Database timeout (transient)-retry should succeed" for infrastructure errors.
• C. Remove is_error: true and return the error details as normal tool content, so Claude reasons about the response as data rather than treating it as a flagged failure condition that biases retry behavior.
• D. Add an error classification step in the agentic loop that intercepts tool errors before Claude sees them, tags each as "retry," "try_alternative," or "escalate," and appends that recommendation to the tool result.

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You are building a structured data extraction system using Claude. The system extracts information from unstructured documents, validates the output using JSON schemas, and maintains high accuracy. It must handle edge cases gracefully and integrate with downstream systems.
Your extraction uses tool use with a JSON schema in which property_type is defined as an enum:
house, apartment, condo, or townhouse. After deployment, 8% of extractions fail schema validation. Investigation reveals that listings mention many uncommon property types--"studio,"
"loft," "duplex," "mobile home," "tiny house," and "converted warehouse"--and new types continue appearing regularly.
What is the most effective long-term solution?

• A. Add an other value to the enum with a separate property_type_detail string field for specifics when other is selected.
• B. Change property_type from an enum to a free-form string and implement a normalization step in post-processing.
• C. Continuously expand the enum to include newly observed property types and add monitoring for additional edge cases.
• D. Add few-shot examples demonstrating how to map unexpected property types to the closest existing enum value.

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You are integrating Claude Code into your Continuous Integration/Continuous Deployment (CI/CD) pipeline. The system runs automated code reviews, generates test cases, and provides feedback on pull requests. You need to design prompts that provide actionable feedback and minimize false positives.
The automated review consistently flags patterns your team uses intentionally--force-unwrapping optionals in test files, using large coordinator classes that follow your established architecture, and importing internally maintained modules marked as deprecated in the public SDK.
Developers dismiss approximately 30% of all findings as project-specific false positives.
Which approach prevents the model from generating these findings in the first place by supplying the project's conventions as persistent context during every review?

• A. Have developers add inline suppression comments at flagged lines and preprocess diffs to exclude suppressed lines before sending code to the model.
• B. Configure the review to analyze only the changed lines in the diff without the surrounding file context, reducing the amount of code the model evaluates.
• C. Document the team's accepted patterns and intentional conventions in the project's CLAUDE.md file so the model receives this context during every review.
• D. Build post-processing keyword filters that suppress findings containing terms such as "force unwrap," "large class," or "deprecated import" before results reach developers.

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The coordinator provides detailed step-by-step instructions to the web search subagent, specifying exact search queries, source priorities, and date filters. Production monitoring reveals three issues: (1) the subagent reports "insufficient results" rather than trying alternative approaches when pre-specified searches fail, (2) research quality drops for emerging topics that don't match expected patterns, and (3) the subagent rarely surfaces valuable tangential sources.
What's the most effective way to improve subagent adaptability?

• A. Remove procedural details entirely, delegating with simple goals like "research X thoroughly" and relying on the subagent's general capabilities.
• B. Add explicit fallback directives to the detailed instructions: "If specified searches yield fewer than N results, attempt alternative query formulations before reporting failure."
• C. Implement a topic classification step where the coordinator categorizes requests as "well-defined" or "exploratory" and uses different instruction styles for each category.
• D. Specify research goals and quality criteria (coverage breadth, source diversity, recency) rather than procedural steps, letting the subagent determine its search strategy.

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