Function calling turns LLMs from text generators into reasoning engines that can interact with external systems. Tool design principles: 1) Clear, self-documenting names: get_customer_order_history (not fetch_data). 2) Descriptions that explain when to use the tool, not just what it does. 3) Atomic tools: each tool does one thing. Avoid combine_and_process_data tools. 4) Idempotent where possible: safely retryable on errors. 5) Validate inputs with Pydantic schemas before execution. Parallel tool calling: OpenAI and Claude support calling multiple tools simultaneously. Implement as asyncio.gather() for concurrent execution - reduces latency significantly for independent tool calls. Error handling: tools should return error messages (not raise exceptions) so LLM can reason about failures and try alternatives. Structured error: {"success": false, "error": "Customer ID 12345 not found", "suggestion": "Use search_customer_by_email instead"}. Tool abuse prevention: add explicit instructions about when NOT to use tools, implement rate limits per tool, log all tool calls for audit, require confirmation for write operations. Safety guardrails: read-only tools by default, write tools require explicit user intent, destructive operations require confirmation. Observability: log every tool call with inputs, outputs, latency, errors for debugging and cost attribution.