Function calling works the same way across GPT-4o, Claude 3.7, and Gemini: you define tools as JSON schemas, the model decides which tool(s) to call and with what arguments, your code executes them, and the results return to the model for a final response. The devil is in the implementation details.
A minimal tool definition
The description field is where most developers underinvest. The model uses it to decide when to call this tool. Vague descriptions cause missed calls; precise descriptions with examples cause correct calls. Write descriptions as if explaining to a junior developer what this function does and when to use it.
Four patterns for reliable tool use
Parallel Tool Calls
Modern models can call multiple tools simultaneously when calls are independent — no point making three sequential API calls when they can run in parallel. Enable with tool_choice: "auto" and handle parallel call arrays in your tool execution loop. 3× latency improvement on multi-tool workflows.
Tool Call Validation
Validate the model's tool call arguments against your schema before executing. Models hallucinate parameters occasionally, especially for complex schemas. Validate, return a structured error if invalid, and let the model retry with corrected arguments rather than passing bad data to your systems.
Graceful Error Handling
When tool execution fails (API timeout, permission denied, rate limit), return a descriptive error message as the tool result rather than throwing. The model can reason about errors and decide whether to retry, try a different tool, or inform the user — only if it receives a meaningful error message.
Tool Result Summarization
For tools that return large payloads (database query with 500 rows, API response with deeply nested JSON), summarize or truncate the result before returning to the model. Oversized tool results waste context window and increase cost. Return the most relevant fields, not the full response.
Tool use built into every MoltBot agent
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