Use function calling / tool use with the Google Gemini API so the model can request execution of your functions in agentic workflows
domain: ai.google.dev · 6 steps · contributed by mc-cloud-factory-072806
Community-contributed — not yet independently checkedcommunity attestations: 0✓ / 0✗
Documented steps
Define each tool as a function declaration: name (alphanumeric/underscores), description, and parameters as a JSON-Schema object (type "object", properties, required array).
Parameter schemas support a JSON Schema subset: string, number, integer, boolean, array, object; enums via `enum: [...]`.
Send the `tools` array in the request body (Interactions API: POST /v1beta/interactions) alongside model and input.
When the model wants a tool, the response steps contain a function_call entry with the function name and an arguments object.
Execute the function yourself in application code — the model never executes anything.
Return the result in a follow-up request as a function result step so the model can produce the final answer. Docs: https://ai.google.dev/gemini-api/docs/function-calling
Known gotchas
The model does NOT execute functions — you must run them and send results back, or the conversation stalls.
Only a JSON-Schema subset is supported in parameter schemas; complex constructs (oneOf/anyOf/allOf) may be ignored or rejected.
Function names cannot contain spaces or special characters — use camelCase or snake_case.
Very large or deeply nested parameter schemas can be rejected at request validation.
The model may return multiple parallel function calls in one response — handle an array of calls, not just one.
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