You are building a customer support resolution agent using the Claude Agent SDK. The agent handles high-ambiguity requests such as returns, billing disputes, and account issues, and has access to backend systems through custom Model Context Protocol (MCP) tools: get_customer, lookup_order, process_refund, and escalate_to_human. Your target is 80%+ first-contact resolution while still knowing when to escalate.
After expanding the agent's MCP tools with delivery-specific capabilities — check_delivery_status, contact_driver, issue_credit, apply_promo_code, update_delivery_address, and reschedule_delivery — the total tool count grows from 4 to 10. Your evaluation suite shows tool selection accuracy dropping from 88% to 71%. Log analysis reveals that most errors involve the agent choosing between semantically overlapping tools: calling issue_credit when process_refund was the correct choice, and calling check_delivery_status when lookup_order already returns the needed data.
Which approach structurally eliminates the semantic overlap identified in the logs as the source of these errors?
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