Function Calling: 8 Tool Use Tips for Developers
Understanding Tool Use and Function Calling
Function calling, also called tool use, lets an AI model request an action outside its text response. Instead of guessing a delivery status, calculating an exchange rate from stale knowledge, or pretending it updated a database, the model can ask your application to run a defined function. Your code performs the real action, returns structured data, and the model uses that result to produce a useful answer.
The important distinction is simple: the model chooses which tool to call and what arguments to suggest; your application remains responsible for executing the tool safely. Use these eight practical tips to build reliable tool-enabled AI features.
1. Start With One Narrow, Valuable Tool
Do not begin with a toolbox of twenty vague actions. Start with a focused function tied to a real user outcome, such as get_order_status, search_product_catalog, or create_support_ticket. A narrow tool is easier for the model to select, simpler to secure, and faster to test.
For example, a get_order_status function should accept an order ID and return a status, estimated delivery date, and tracking URL. It should not accept a generic query string that gives the model broad, unpredictable access to your order system.
2. Write Schemas Like an API Contract
Your function definition is the model's instruction manual. Give every parameter a clear name, type, description, and valid values. Mark fields as required only when they are actually essential. If a date must be formatted as YYYY-MM-DD, say so explicitly. If a status can only be pending, paid, shipped, or cancelled, expose that limited set.
- Use specific names: customer_email is clearer than input.
- Describe business rules: explain whether IDs are internal or customer-facing.
- Prefer enums: constrained choices reduce malformed calls.
- Keep outputs structured: return predictable JSON fields that the model can interpret.
Good schemas reduce retries, lower token usage, and make model behavior more consistent across providers.
3. Validate Every Argument Server-Side
Tool arguments are model-generated input, not trusted application input. Validate types, required fields, formats, lengths, allowed values, and authorization before invoking any downstream service. Treat a request to delete a record or send an email with the same caution as a request coming from a public web form.
Never let tool arguments become raw SQL, shell commands, file paths, or URLs without strict validation and parameterization. The model can misunderstand a prompt, and a user can intentionally try to influence its tool call.
4. Separate Read Actions From Write Actions
Read-only tools such as search_inventory and get_invoice are the best place to begin. For actions that change state, add stronger controls. Require confirmation before placing an order, sending a message, issuing a refund, or deleting data. Include a preview step when possible, then execute only after the user explicitly approves the final details.
For write tools, use idempotency keys so a model retry cannot create duplicate orders or support tickets. Return a durable action ID and store an audit record containing the user, tool name, validated arguments, result, and timestamp.
5. Build a Clear Tool Execution Loop
A typical flow has four stages: send the user message and available tools to the model, inspect the response for tool calls, execute approved calls in your application, and send tool results back to the model for a final answer. The model may request multiple calls, so set a sensible maximum loop count to prevent runaway behavior.
Return factual results, including failures. If a CRM lookup times out, send a structured timeout error instead of inventing a customer record. The model can then explain the limitation or ask the user to try again.
6. Make Failures Useful
Design tool responses for recovery. A useful error says what failed and what can happen next: invalid order ID, permission denied, unavailable service, or no matching records. Avoid exposing internal stack traces, secrets, database details, or vendor credentials.
Also distinguish between a valid empty result and an error. An empty product search should return an empty items list; it should not look like a failed API request.
7. Test Real Conversations, Not Just JSON
Unit-test schema validation and tool handlers, then test multi-turn conversations. Include ambiguous requests, missing details, invalid IDs, unauthorized users, service outages, duplicate requests, and prompt-injection attempts. Check whether the model asks an appropriate follow-up question rather than calling a tool with invented arguments.
Log tool selection rate, validation failures, execution latency, error rate, retries, and cost per completed task. These metrics show whether a tool is genuinely helping users or merely adding expensive complexity.
8. Keep Model Costs Practical With 59API
Tool use often involves several model turns: selecting a function, reading the returned data, and generating a final response. That makes pricing and provider compatibility important. 59API is a low-cost, pay-as-you-go AI API relay with access to native official-quality Claude models, including Opus, Sonnet, Haiku, and Fable, plus GPT models without a model downgrade.
Its API base URL is https://api.59api.com, and its compatibility with Claude Code, Codex, and OpenAI SDK workflows can reduce integration friction when you are evaluating tool-enabled applications. Teams can select a lower-cost model for routine classification or lookup tasks and reserve higher-capability models for complex planning, while keeping their tool contract consistent. 59API also offers a referral rebate. For developers building production tool use on a budget, signing up for 59API is a practical way to test model choices and control pay-as-you-go spend.