Tools

A tool is a function the model can decide to call. On the server it is a description, a schema and an implementation; in the UI it is another kind of message part, sitting in the same ordered parts array as the text around it.

Defining one

app/api/chat+api.ts

import { anthropic } from '@ai-sdk/anthropic';
import { convertToModelMessages, streamText, stepCountIs, tool } from 'ai';
import { z } from 'zod';

const tools = {
  getWeather: tool({
    description: 'Get the current weather for a city.',
    inputSchema: z.object({
      city: z.string().describe('City name, e.g. "Amsterdam"'),
    }),
    execute: async ({ city }) => {
      const data = await weatherApi.current(city);
      return { city, temperature: data.temp, conditions: data.summary };
    },
  }),
};

export async function POST(request: Request) {
  const { messages } = await request.json();

  const result = streamText({
    model: anthropic('claude-sonnet-5'),
    messages: convertToModelMessages(messages),
    tools,
    // Without this the model calls the tool and stops. This lets it call the
    // tool, read the result, and answer — up to five steps.
    stopWhen: stepCountIs(5),
  });

  return result.toUIMessageStreamResponse();
}

The description and the schema's .describe() calls are the model's only information about when to use a tool. They are prompt, not documentation — write them for the model.

Rendering a tool call

Tool parts arrive as tool-<name> with a state that moves through input-streaming → input-available → output-available, or lands on output-error. Rendering the intermediate states is what makes a slow tool feel like progress instead of a hang.

Item is the right shape for this — media, title, description, in a row.

import { Item, Shimmer, Spinner, SearchIcon, CheckCircleIcon, AlertTriangleIcon } from 'panelui-native';

function ToolCall({ part }) {
  switch (part.state) {
    case 'input-streaming':
    case 'input-available':
      return (
        <Item variant="muted" size="sm">
          <Item.Media variant="icon">
            <Spinner size="sm" />
          </Item.Media>
          <Item.Content>
            <Item.Title>
              <Shimmer textClassName="text-sm font-medium">
                Checking the weather…
              </Shimmer>
            </Item.Title>
          </Item.Content>
        </Item>
      );

    case 'output-available':
      return (
        <Item variant="muted" size="sm">
          <Item.Media variant="icon">
            <CheckCircleIcon size={16} />
          </Item.Media>
          <Item.Content>
            <Item.Title>{part.output.city}</Item.Title>
            <Item.Description>
              {part.output.temperature}° · {part.output.conditions}
            </Item.Description>
          </Item.Content>
        </Item>
      );

    case 'output-error':
      return (
        <Item variant="outline" size="sm">
          <Item.Media variant="icon">
            <AlertTriangleIcon size={16} />
          </Item.Media>
          <Item.Content>
            <Item.Title>Could not check the weather</Item.Title>
            <Item.Description>{part.errorText}</Item.Description>
          </Item.Content>
        </Item>
      );

    default:
      return null;
  }
}

Placing it in the turn

Because parts is ordered, walking it in order puts the tool call exactly where the model produced it — between the sentence that led to it and the sentence that followed.

<Message.Content>
  {message.parts.map((part, index) => {
    if (part.type === 'text') {
      return (
        <Message.Bubble key={index}>
          <Message.BubbleContent>{part.text}</Message.BubbleContent>
        </Message.Bubble>
      );
    }

    if (part.type === 'tool-getWeather') {
      return <ToolCall key={index} part={part} />;
    }

    return null;
  })}
</Message.Content>

Note the tool call sits outside Message.Bubble — it is a step the assistant took, not something it said, and it reads better as its own row.

Tools that run on the device

Some tools have to run client-side: reading location, taking a photo, writing to local storage. Leave execute off the server definition and handle the call in onToolCall, then hand the result back with addToolOutput.

const { messages, addToolOutput } = useChat({
  transport,
  onToolCall: async ({ toolCall }) => {
    if (toolCall.toolName === 'getLocation') {
      const { coords } = await Location.getCurrentPositionAsync();
      addToolOutput({
        toolCallId: toolCall.toolCallId,
        output: { latitude: coords.latitude, longitude: coords.longitude },
      });
    }
  },
});

A client tool that needs a permission can block for as long as the user takes to answer the dialog. Handle the denial — return an output saying the location is unavailable rather than never calling addToolOutput, or the turn hangs forever.

Confirming before acting

For anything destructive or expensive, do not give the tool an execute at all. Render the pending call, and only submit the output once the user agrees.

case 'input-available':
  return (
    <Item variant="outline" size="sm" className="flex-col items-start gap-2">
      <Item.Content>
        <Item.Title>Send this email?</Item.Title>
        <Item.Description>To {part.input.to} — “{part.input.subject}”</Item.Description>
      </Item.Content>
      <Item.Footer>
        <Button
          size="sm"
          variant="ghost"
          onPress={() =>
            addToolOutput({
              toolCallId: part.toolCallId,
              output: { sent: false, reason: 'declined by user' },
            })
          }
        >
          Cancel
        </Button>
        <Button size="sm" onPress={() => send(part.input)}>Send</Button>
      </Item.Footer>
    </Item>
  );

Telling the model why something did not happen matters — declined by user lets it respond sensibly, where a bare failure invites it to try again.

Defining one

Rendering a tool call

Placing it in the turn

Tools that run on the device

Confirming before acting

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