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Tool Use and Function Calling: A 2026 Developer Guide

What Tool Use and Function Calling Actually Mean

Tool use lets an AI model request capabilities beyond generating text: checking inventory, querying a database, or creating a support ticket. Function calling is a structured way to make those requests, usually through named functions with JSON arguments. In a typical application-managed workflow, the model proposes a call; your application validates and executes it. The model does not automatically gain access to your systems.

For 2026, the best practice is to treat tool calling as a controlled application protocol, not a shortcut around authorization, validation, or error handling.

Build the Complete Tool-Calling Loop

Consider an assistant answering, “Where is order A123?” A reliable implementation follows these steps:

• Declare a tool: Describe lookup_order, its purpose, and the accepted order_id argument.
• Send the conversation: Include the user’s question and tool definitions in the model request.
• Inspect the response: The model may answer directly or request one or more tool calls. Do not assume every response contains ordinary text.
• Validate and execute: Check arguments and permissions before calling your order service.
• Return the result: Send structured output linked to the original tool-call identifier.
• Continue the conversation: Request the next model response, which may summarize the result or request another tool.

Set a maximum number of iterations. Otherwise, a failing dependency or ambiguous task can create an expensive loop.

Design Small, Explicit Tool Schemas

Start with narrow tools rather than one unrestricted “run_anything” function. For an OpenAI Chat Completions-style tool definition, use type “function” and put name, description, and parameters inside the function object. An example parameters schema is:

{"type":"object","properties":{"order_id":{"type":"string","description":"The order identifier, such as A123"}},"required":["order_id"],"additionalProperties":false}

Where supported, enable strict schema adherence. Check the endpoint’s requirements: some strict implementations require every property to be listed as required, with nullable types used for optional values. Strict formatting improves structural reliability, but it does not prove that an order exists or that the requester may access it.

Do not copy request objects blindly between APIs. OpenAI Responses, Chat Completions, and Anthropic Messages use different tool and message structures.

Preserve Call IDs and Handle Failures

With OpenAI Chat Completions, preserve the assistant message containing tool_calls, then append a tool message for each result using its matching tool_call_id. With Anthropic Messages, return tool_result blocks referencing the corresponding tool_use_id. Follow each API’s message-ordering rules.

Return concise, predictable results, such as an object containing status, order_id, and estimated_delivery. For failures, provide a stable error code and a safe explanation. Never disguise a timeout as “order not found.” Retry transient failures selectively; use idempotency keys before retrying operations that create charges, bookings, or records.

Enforce Security Outside the Model

• Authorize every execution: Derive account access from authenticated application state, not a model-supplied customer ID.
• Separate reading from writing: Require explicit confirmation for purchases, deletions, or other consequential changes.
• Treat retrieved content as untrusted: Instructions inside documents or tool results must not override your application’s policies.
• Limit capabilities: Apply timeouts, request quotas, destination allowlists, and narrowly scoped credentials.
• Protect observability data: Log call IDs, latency, and error categories while redacting secrets and sensitive payloads.

Optimize Cost Without Weakening Reliability

Tool definitions and returned data consume context tokens. Send only relevant tools, keep descriptions precise, and return selected fields instead of entire database rows. Cache suitable read-only results and parallelize independent calls when supported; dependent calls must remain ordered.

Evaluate models on successful task completion, invalid arguments, latency, and total cost per completed workflow. A cheaper request is not cheaper overall if it repeatedly selects the wrong tool.

Use a Low-Cost API Layer

59API offers inexpensive, pay-as-you-go access to native, official-quality Claude and GPT models without model downgrades. Its compatibility with Claude Code, Codex, and OpenAI SDKs makes it a practical option for cost-conscious development. Configure your client using https://api.59api.com and the documented endpoint path, then test the tool-calling features your workflow requires.

59API also offers a referral rebate. If you are building a tool-enabled assistant, consider signing up and benchmarking a small, representative workflow before expanding production traffic.

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