Handle approvals and user input

Surface Claude’s approval requests and clarifying questions to users, then return their decisions to the SDK.

While working on a task, Claude sometimes needs to check in with users. It might need permission before deleting files, or need to ask which database to use for a new project. Your application needs to surface these requests to users so Claude can continue with their input.

Claude requests user input in two situations: when it needs permission to use a tool (like deleting files or running commands), and when it has clarifying questions (via the AskUserQuestion tool). Both trigger your canUseTool callback, which pauses execution until you return a response. This is different from normal conversation turns where Claude finishes and waits for your next message.

For clarifying questions, Claude generates the questions and options. Your role is to present them to users and return their selections. You can’t add your own questions to this flow; if you need to ask users something yourself, do that separately in your application logic.

The callback can stay pending indefinitely. Execution remains paused until your callback returns, and the SDK only cancels the wait when the query itself is cancelled. If a user might take longer to respond than your process can reasonably stay running, return the defer hook decision, which lets the process exit and resume later from the persisted session.

This guide shows you how to detect each type of request and respond appropriately.

Detect when Claude needs input

Pass a canUseTool callback in your query options. The callback fires whenever Claude needs user input, receiving the tool name and input as arguments:

Python

async def handle_tool_request(tool_name, input_data, context):
    # Prompt user and return allow or deny
    ...


options = ClaudeAgentOptions(can_use_tool=handle_tool_request)

async function handleToolRequest(toolName, input, options) {
  // options includes { signal: AbortSignal, suggestions?: PermissionUpdate[] }
  // Prompt user and return allow or deny
}

const options = { canUseTool: handleToolRequest };

The callback fires in two cases:

1. Tool needs approval: Claude wants to use a tool that isn’t auto-approved by a permission rule or permission mode. Check tool_name for the tool (e.g., "Bash", "Write").
2. Claude asks a question: Claude calls the AskUserQuestion tool. Check if tool_name == "AskUserQuestion" to handle it differently. If you specify a tools array, include AskUserQuestion for this to work. See Handle clarifying questions for details.

The callback never fires for auto-approved tools. Any approval earlier in the permission evaluation flow, an allow rule or a mode like acceptEdits or bypassPermissions, resolves the call before canUseTool is consulted. If you list a tool bare in allowed_tools, a canUseTool check for that tool never runs unless an ask rule or plan mode routes the call back to a prompt. For logic that must apply to every tool call, use a PreToolUse hook, which executes before the rest of the flow and can allow, deny, or modify requests.

AskUserQuestion, MCP tools marked requiresUserInteraction, and connector tools your organization set to ask reach the callback even when an allow rule matches. In dontAsk mode these calls are denied instead, without invoking the callback.

You can also use the PermissionRequest hook to send external notifications (Slack, email, push) when Claude is waiting for approval.

Handle tool approval requests

Once you’ve passed a canUseTool callback in your query options, it fires when Claude wants to use a tool that nothing earlier in the permission flow has approved. Your callback receives three arguments:

Argument	Description
toolName	The name of the tool Claude wants to use (e.g., "Bash", "Write", "Edit")
input	The parameters Claude is passing to the tool. Contents vary by tool.
options (TS) / context (Python)	Additional context including optional suggestions (proposed PermissionUpdate entries to avoid re-prompting) and a cancellation signal. In TypeScript, signal is an AbortSignal; in Python, the signal field is reserved for future use. See ToolPermissionContext for Python.

The input object contains tool-specific parameters. Common examples:

Tool	Input fields
Bash	command, description, timeout
Write	file_path, content
Edit	file_path, old_string, new_string
Read	file_path, offset, limit

See the SDK reference for complete input schemas: Python | TypeScript.

You can display this information to the user so they can decide whether to allow or reject the action, then return the appropriate response.

The following example asks Claude to create and delete a test file. When Claude attempts each operation, the callback prints the tool request to the terminal and prompts for y/n approval.

Python

import asyncio

from claude_agent_sdk import ClaudeAgentOptions, ResultMessage, query
from claude_agent_sdk.types import (
    HookMatcher,
    PermissionResultAllow,
    PermissionResultDeny,
    ToolPermissionContext,
)


async def can_use_tool(
    tool_name: str, input_data: dict, context: ToolPermissionContext
) -> PermissionResultAllow | PermissionResultDeny:
    # Display the tool request
    print(f"\nTool: {tool_name}")
    if tool_name == "Bash":
        print(f"Command: {input_data.get('command')}")
        if input_data.get("description"):
            print(f"Description: {input_data.get('description')}")
    else:
        print(f"Input: {input_data}")

    # Get user approval
    response = input("Allow this action? (y/n): ")

    # Return allow or deny based on user's response
    if response.lower() == "y":
        # Allow: tool executes with the original (or modified) input
        return PermissionResultAllow(updated_input=input_data)
    else:
        # Deny: tool doesn't execute, Claude sees the message
        return PermissionResultDeny(message="User denied this action")


# Required workaround: dummy hook keeps the stream open for can_use_tool
async def dummy_hook(input_data, tool_use_id, context):
    return {"continue_": True}


async def prompt_stream():
    yield {
        "type": "user",
        "message": {
            "role": "user",
            "content": "Create a test file in /tmp and then delete it",
        },
    }


async def main():
    async for message in query(
        prompt=prompt_stream(),
        options=ClaudeAgentOptions(
            can_use_tool=can_use_tool,
            hooks={"PreToolUse": [HookMatcher(matcher=None, hooks=[dummy_hook])]},
        ),
    ):
        if isinstance(message, ResultMessage) and message.subtype == "success":
            print(message.result)


asyncio.run(main())

import { query } from "@anthropic-ai/claude-agent-sdk";
import * as readline from "readline";

// Helper to prompt user for input in the terminal
function prompt(question: string): Promise<string> {
  const rl = readline.createInterface({
    input: process.stdin,
    output: process.stdout
  });
  return new Promise((resolve) =>
    rl.question(question, (answer) => {
      rl.close();
      resolve(answer);
    })
  );
}

for await (const message of query({
  prompt: "Create a test file in /tmp and then delete it",
  options: {
    canUseTool: async (toolName, input) => {
      // Display the tool request
      console.log(`\nTool: ${toolName}`);
      if (toolName === "Bash") {
        console.log(`Command: ${input.command}`);
        if (input.description) console.log(`Description: ${input.description}`);
      } else {
        console.log(`Input: ${JSON.stringify(input, null, 2)}`);
      }

      // Get user approval
      const response = await prompt("Allow this action? (y/n): ");

      // Return allow or deny based on user's response
      if (response.toLowerCase() === "y") {
        // Allow: tool executes with the original (or modified) input
        return { behavior: "allow", updatedInput: input };
      } else {
        // Deny: tool doesn't execute, Claude sees the message
        return { behavior: "deny", message: "User denied this action" };
      }
    }
  }
})) {
  if ("result" in message) console.log(message.result);
}

In Python, can_use_tool requires streaming mode. When you pass a finite message stream through query(prompt=generator) or ClaudeSDKClient.connect(prompt=async_iterable), the SDK closes the input stream after the last message, before the permission callback can be invoked, unless a registered hook or in-process MCP server is keeping it open. The example above keeps it open with a PreToolUse hook that returns {"continue_": True}. Connecting with no prompt and sending messages through ClaudeSDKClient.query() keeps the stream open on its own and needs no hook.

This example uses a y/n flow where any input other than y is treated as a denial. In practice, you might build a richer UI that lets users modify the request, provide feedback, or redirect Claude entirely. See Respond to tool requests for all the ways you can respond.

Respond to tool requests

Your callback returns one of two response types:

Response	Python	TypeScript
Allow	PermissionResultAllow(updated_input=...)	{ behavior: "allow", updatedInput }
Deny	PermissionResultDeny(message=...)	{ behavior: "deny", message }

When allowing, the tool runs with the input Claude requested unless you return a modified input, updatedInput in TypeScript or updated_input in Python. Before v2.1.207, Claude Code rejected an allow result that omitted updatedInput and denied the tool call with a validation error.

When denying, provide a message explaining why. Claude sees this message and may adjust its approach.

Python

from claude_agent_sdk.types import PermissionResultAllow, PermissionResultDeny

# Allow the tool to execute
return PermissionResultAllow(updated_input=input_data)

# Block the tool
return PermissionResultDeny(message="User rejected this action")

// Allow the tool to execute
return { behavior: "allow", updatedInput: input };

// Block the tool
return { behavior: "deny", message: "User rejected this action" };

Beyond allowing or denying, you can modify the tool’s input or provide context that helps Claude adjust its approach:

• Approve: let the tool execute as Claude requested
• Approve with changes: modify the input before execution (e.g., sanitize paths, add constraints)
• Approve and remember: echo a suggested permission rule back so matching calls skip the prompt next time
• Reject: block the tool and tell Claude why
• Suggest alternative: block but guide Claude toward what the user wants instead
• Redirect entirely: use streaming input to send Claude a completely new instruction

Approve

The user approves the action as-is. Pass through the input from your callback unchanged and the tool executes exactly as Claude requested.

Python

async def can_use_tool(tool_name, input_data, context):
    print(f"Claude wants to use {tool_name}")
    approved = await ask_user("Allow this action?")

    if approved:
        return PermissionResultAllow(updated_input=input_data)
    return PermissionResultDeny(message="User declined")

canUseTool: async (toolName, input) => {
  console.log(`Claude wants to use ${toolName}`);
  const approved = await askUser("Allow this action?");

  if (approved) {
    return { behavior: "allow", updatedInput: input };
  }
  return { behavior: "deny", message: "User declined" };
};

Approve with changes

The user approves but wants to modify the request first. You can change the input before the tool executes. Claude sees the result but isn’t told you changed anything. Useful for sanitizing parameters, adding constraints, or scoping access.

Python

async def can_use_tool(tool_name, input_data, context):
    if tool_name == "Bash":
        # User approved, but scope all commands to sandbox
        sandboxed_input = {**input_data}
        sandboxed_input["command"] = input_data["command"].replace(
            "/tmp", "/tmp/sandbox"
        )
        return PermissionResultAllow(updated_input=sandboxed_input)
    return PermissionResultAllow(updated_input=input_data)

canUseTool: async (toolName, input) => {
  if (toolName === "Bash") {
    // User approved, but scope all commands to sandbox
    const sandboxedInput = {
      ...input,
      command: input.command.replace("/tmp", "/tmp/sandbox")
    };
    return { behavior: "allow", updatedInput: sandboxedInput };
  }
  return { behavior: "allow", updatedInput: input };
};

Approve and remember

The user approves and doesn’t want to be asked again for this kind of call. The third callback argument carries suggestions, an array of ready-made PermissionUpdate entries. Echo one back in updatedPermissions to apply it. A suggestion with the localSettings destination writes the rule to .claude/settings.local.json so future sessions skip the prompt for matching calls.

The Python example requires claude-agent-sdk 0.1.80 or later.

Python

async def can_use_tool(tool_name, input_data, context):
    choice = await ask_user(f"Allow {tool_name}?", ["once", "always", "no"])

    if choice == "always":
        persist = [
            s for s in context.suggestions if s.destination == "localSettings"
        ]
        return PermissionResultAllow(
            updated_input=input_data, updated_permissions=persist
        )
    if choice == "once":
        return PermissionResultAllow(updated_input=input_data)
    return PermissionResultDeny(message="User declined")

canUseTool: async (toolName, input, { suggestions = [] }) => {
  const choice = await askUser(`Allow ${toolName}?`, ["once", "always", "no"]);

  if (choice === "always") {
    const persist = suggestions.filter(
      (s) => s.destination === "localSettings"
    );
    return {
      behavior: "allow",
      updatedInput: input,
      updatedPermissions: persist
    };
  }
  if (choice === "once") {
    return { behavior: "allow", updatedInput: input };
  }
  return { behavior: "deny", message: "User declined" };
};

Reject

The user doesn’t want this action to happen. Block the tool and provide a message explaining why. Claude sees this message and may try a different approach.

Python

async def can_use_tool(tool_name, input_data, context):
    approved = await ask_user(f"Allow {tool_name}?")

    if not approved:
        return PermissionResultDeny(message="User rejected this action")
    return PermissionResultAllow(updated_input=input_data)

canUseTool: async (toolName, input) => {
  const approved = await askUser(`Allow ${toolName}?`);

  if (!approved) {
    return {
      behavior: "deny",
      message: "User rejected this action"
    };
  }
  return { behavior: "allow", updatedInput: input };
};

Suggest alternative

The user doesn’t want this specific action, but has a different idea. Block the tool and include guidance in your message. Claude will read this and decide how to proceed based on your feedback.

Python

async def can_use_tool(tool_name, input_data, context):
    if tool_name == "Bash" and "rm" in input_data.get("command", ""):
        # User doesn't want to delete, suggest archiving instead
        return PermissionResultDeny(
            message="User doesn't want to delete files. They asked if you could compress them into an archive instead."
        )
    return PermissionResultAllow(updated_input=input_data)

canUseTool: async (toolName, input) => {
  if (toolName === "Bash" && input.command.includes("rm")) {
    // User doesn't want to delete, suggest archiving instead
    return {
      behavior: "deny",
      message:
        "User doesn't want to delete files. They asked if you could compress them into an archive instead."
    };
  }
  return { behavior: "allow", updatedInput: input };
};

Redirect entirely

For a complete change of direction (not just a nudge), use streaming input to send Claude a new instruction directly. This bypasses the current tool request and gives Claude entirely new instructions to follow.

Handle clarifying questions

When Claude needs more direction on a task with multiple valid approaches, it calls the AskUserQuestion tool. This triggers your canUseTool callback with toolName set to AskUserQuestion. The input contains Claude’s questions as multiple-choice options, which you display to the user and return their selections.

Clarifying questions are especially common in plan mode, where Claude explores the codebase and asks questions before proposing a plan. This makes plan mode ideal for interactive workflows where you want Claude to gather requirements before making changes.

The following steps show how to handle clarifying questions:

1. Pass a canUseTool callback

   Pass a canUseTool callback in your query options. By default, AskUserQuestion is available. If you specify a tools array to restrict Claude’s capabilities (for example, a read-only agent with only Read, Glob, and Grep), include AskUserQuestion in that array. Otherwise, Claude won’t be able to ask clarifying questions:

   Python

   async for message in query(
       prompt="Analyze this codebase",
       options=ClaudeAgentOptions(
           # Include AskUserQuestion in your tools list
           tools=["Read", "Glob", "Grep", "AskUserQuestion"],
           can_use_tool=can_use_tool,
       ),
   ):
       print(message)
   

   for await (const message of query({
     prompt: "Analyze this codebase",
     options: {
       // Include AskUserQuestion in your tools list
       tools: ["Read", "Glob", "Grep", "AskUserQuestion"],
       canUseTool: async (toolName, input) => {
         // Handle clarifying questions here
       }
     }
   })) {
     console.log(message);
   }
   
2. Detect AskUserQuestion

   In your callback, check if toolName equals AskUserQuestion to handle it differently from other tools:

   Python

   async def can_use_tool(tool_name: str, input_data: dict, context):
       if tool_name == "AskUserQuestion":
           # Your implementation to collect answers from the user
           return await handle_clarifying_questions(input_data)
       # Handle other tools normally
       return await prompt_for_approval(tool_name, input_data)
   

   canUseTool: async (toolName, input) => {
     if (toolName === "AskUserQuestion") {
       // Your implementation to collect answers from the user
       return handleClarifyingQuestions(input);
     }
     // Handle other tools normally
     return promptForApproval(toolName, input);
   };
   
3. Parse the question input

   The input contains Claude’s questions in a questions array. Each question has a question (the text to display), options (the choices), and multiSelect (whether multiple selections are allowed):

   {
     "questions": [
       {
         "question": "How should I format the output?",
         "header": "Format",
         "options": [
           { "label": "Summary", "description": "Brief overview" },
           { "label": "Detailed", "description": "Full explanation" }
         ],
         "multiSelect": false
       },
       {
         "question": "Which sections should I include?",
         "header": "Sections",
         "options": [
           { "label": "Introduction", "description": "Opening context" },
           { "label": "Conclusion", "description": "Final summary" }
         ],
         "multiSelect": true
       }
     ]
   }
   

   See Question format for full field descriptions.

4. Collect answers from the user

   Present the questions to the user and collect their selections. How you do this depends on your application: a terminal prompt, a web form, a mobile dialog, etc.

5. Return answers to Claude

   Build the answers object as a record where each key is the question text and each value is the selected option’s label:

   From the question object	Use as
   question field (e.g., "How should I format the output?")	Key
   Selected option’s label field (e.g., "Summary")	Value

   For multi-select questions, pass an array of labels or join them with ", ". If you support free-text input, use the user’s custom text as the value.

   Python

   return PermissionResultAllow(
       updated_input={
           "questions": input_data.get("questions", []),
           "answers": {
               "How should I format the output?": "Summary",
               "Which sections should I include?": ["Introduction", "Conclusion"],
           },
       }
   )
   

   return {
     behavior: "allow",
     updatedInput: {
       questions: input.questions,
       answers: {
         "How should I format the output?": "Summary",
         "Which sections should I include?": "Introduction, Conclusion"
       }
     }
   };
   

Question format

The input contains Claude’s generated questions in a questions array. Each question has these fields:

Field	Description
question	The full question text to display
header	Short label for the question (max 12 characters)
options	Array of 2-4 choices, each with label and description. TypeScript: optionally preview (see below)
multiSelect	If true, users can select multiple options

The structure your callback receives:

{
  "questions": [
    {
      "question": "How should I format the output?",
      "header": "Format",
      "options": [
        { "label": "Summary", "description": "Brief overview of key points" },
        { "label": "Detailed", "description": "Full explanation with examples" }
      ],
      "multiSelect": false
    }
  ]
}

Option previews (TypeScript)

toolConfig.askUserQuestion.previewFormat adds a preview field to each option so your app can show a visual mockup alongside the label. Without this setting, Claude does not generate previews and the field is absent.

previewFormat	preview contains
unset (default)	Field is absent. Claude does not generate previews.
"markdown"	ASCII art and fenced code blocks
"html"	A styled <div> fragment (the SDK rejects <script>, <style>, and <!DOCTYPE> before your callback runs)

The format applies to all questions in the session. Claude includes preview on options where a visual comparison helps (layout choices, color schemes) and omits it where one wouldn’t (yes/no confirmations, text-only choices). Check for undefined before rendering.

import { query } from "@anthropic-ai/claude-agent-sdk";

for await (const message of query({
  prompt: "Help me choose a card layout",
  options: {
    toolConfig: {
      askUserQuestion: { previewFormat: "html" }
    },
    canUseTool: async (toolName, input) => {
      // input.questions[].options[].preview is an HTML string or undefined
      return { behavior: "allow", updatedInput: input };
    }
  }
})) {
  // ...
}

An option with an HTML preview:

{
  "label": "Compact",
  "description": "Title and metric value only",
  "preview": "<div style=\"padding:12px;border:1px solid #ddd;border-radius:8px\"><div style=\"font-size:12px;color:#666\">Active users</div><div style=\"font-size:28px;font-weight:600\">1,284</div></div>"
}

Response format

Return an answers object mapping each question’s question field to the selected option’s label:

Field	Description
questions	Pass through the original questions array (required for tool processing)
answers	Object where keys are question text and values are selected labels
response	Optional freeform reply the user typed instead of answering the structured questions

For multi-select questions, pass an array of labels or join them with ", ". For per-question free text such as an “Other” option, put the user’s text in answers[question] as shown in Support free-text input. Set response only when your UI lets the user dismiss the question card and type a general reply that isn’t an answer to any specific question. When response is set, Claude receives “The user responded: …” instead of the per-question answer list.

{
  "questions": [
    // ...
  ],
  "answers": {
    "How should I format the output?": "Summary",
    "Which sections should I include?": ["Introduction", "Conclusion"]
  }
}

Support free-text input

Claude’s predefined options won’t always cover what users want. To let users type their own answer:

• Display an additional “Other” choice after Claude’s options that accepts text input
• Use the user’s custom text as the answer value (not the word “Other”)

See the complete example below for a full implementation.

Complete example

Claude asks clarifying questions when it needs user input to proceed. For example, when asked to help decide on a tech stack for a mobile app, Claude might ask about cross-platform vs native, backend preferences, or target platforms. These questions help Claude make decisions that match the user’s preferences rather than guessing.

This example handles those questions in a terminal application. Here’s what happens at each step:

1. Route the request: The canUseTool callback checks if the tool name is "AskUserQuestion" and routes to a dedicated handler
2. Display questions: The handler loops through the questions array and prints each question with numbered options
3. Collect input: The user can enter a number to select an option, or type free text directly (e.g., “jquery”, “i don’t know”)
4. Map answers: The code checks if input is numeric (uses the option’s label) or free text (uses the text directly)
5. Return to Claude: The response includes both the original questions array and the answers mapping

Save the TypeScript version as ask.ts and run it with npx tsx ask.ts, or save the Python version as ask.py and run it with python ask.py.

Python

import asyncio

from claude_agent_sdk import ClaudeAgentOptions, ResultMessage, query
from claude_agent_sdk.types import HookMatcher, PermissionResultAllow


def parse_response(response: str, options: list) -> str:
    """Parse user input as option number(s) or free text."""
    try:
        indices = [int(s.strip()) - 1 for s in response.split(",")]
        labels = [options[i]["label"] for i in indices if 0 <= i < len(options)]
        return ", ".join(labels) if labels else response
    except ValueError:
        return response


async def handle_ask_user_question(input_data: dict) -> PermissionResultAllow:
    """Display Claude's questions and collect user answers."""
    answers = {}

    for q in input_data.get("questions", []):
        print(f"\n{q['header']}: {q['question']}")

        options = q["options"]
        for i, opt in enumerate(options):
            print(f"  {i + 1}. {opt['label']} - {opt['description']}")
        if q.get("multiSelect"):
            print("  (Enter numbers separated by commas, or type your own answer)")
        else:
            print("  (Enter a number, or type your own answer)")

        response = input("Your choice: ").strip()
        answers[q["question"]] = parse_response(response, options)

    return PermissionResultAllow(
        updated_input={
            "questions": input_data.get("questions", []),
            "answers": answers,
        }
    )


async def can_use_tool(
    tool_name: str, input_data: dict, context
) -> PermissionResultAllow:
    # Route AskUserQuestion to our question handler
    if tool_name == "AskUserQuestion":
        return await handle_ask_user_question(input_data)
    # Auto-approve other tools for this example
    return PermissionResultAllow(updated_input=input_data)


async def prompt_stream():
    yield {
        "type": "user",
        "message": {
            "role": "user",
            "content": "Help me decide on the tech stack for a new mobile app",
        },
    }


# Required workaround: dummy hook keeps the stream open for can_use_tool
async def dummy_hook(input_data, tool_use_id, context):
    return {"continue_": True}


async def main():
    async for message in query(
        prompt=prompt_stream(),
        options=ClaudeAgentOptions(
            can_use_tool=can_use_tool,
            hooks={"PreToolUse": [HookMatcher(matcher=None, hooks=[dummy_hook])]},
        ),
    ):
        if isinstance(message, ResultMessage) and message.subtype == "success":
            print(message.result)


asyncio.run(main())

import { query } from "@anthropic-ai/claude-agent-sdk";
import * as readline from "readline/promises";

// Helper to prompt user for input in the terminal
async function prompt(question: string): Promise<string> {
  const rl = readline.createInterface({ input: process.stdin, output: process.stdout });
  const answer = await rl.question(question);
  rl.close();
  return answer;
}

// Parse user input as option number(s) or free text
function parseResponse(response: string, options: any[]): string {
  const indices = response.split(",").map((s) => parseInt(s.trim()) - 1);
  const labels = indices
    .filter((i) => !isNaN(i) && i >= 0 && i < options.length)
    .map((i) => options[i].label);
  return labels.length > 0 ? labels.join(", ") : response;
}

// Display Claude's questions and collect user answers
async function handleAskUserQuestion(input: any) {
  const answers: Record<string, string> = {};

  for (const q of input.questions) {
    console.log(`\n${q.header}: ${q.question}`);

    const options = q.options;
    options.forEach((opt: any, i: number) => {
      console.log(`  ${i + 1}. ${opt.label} - ${opt.description}`);
    });
    if (q.multiSelect) {
      console.log("  (Enter numbers separated by commas, or type your own answer)");
    } else {
      console.log("  (Enter a number, or type your own answer)");
    }

    const response = (await prompt("Your choice: ")).trim();
    answers[q.question] = parseResponse(response, options);
  }

  // Return the answers to Claude (must include original questions)
  return {
    behavior: "allow",
    updatedInput: { questions: input.questions, answers }
  };
}

async function main() {
  for await (const message of query({
    prompt: "Help me decide on the tech stack for a new mobile app",
    options: {
      canUseTool: async (toolName, input) => {
        // Route AskUserQuestion to our question handler
        if (toolName === "AskUserQuestion") {
          return handleAskUserQuestion(input);
        }
        // Auto-approve other tools for this example
        return { behavior: "allow", updatedInput: input };
      }
    }
  })) {
    if ("result" in message) console.log(message.result);
  }
}

main();

Limitations

• Subagents: AskUserQuestion is not currently available in subagents spawned via the Agent tool
• Question limits: each AskUserQuestion call supports 1-4 questions with 2-4 options each

Other ways to get user input

The canUseTool callback and AskUserQuestion tool cover most approval and clarification scenarios, but the SDK offers other ways to get input from users:

Streaming input

Use streaming input when you need to:

• Interrupt the agent mid-task: send a cancel signal or change direction while Claude is working
• Provide additional context: add information Claude needs without waiting for it to ask
• Build chat interfaces: let users send follow-up messages during long-running operations

Streaming input is ideal for conversational UIs where users interact with the agent throughout execution, not just at approval checkpoints.

Custom tools

Use custom tools when you need to:

• Collect structured input: build forms, wizards, or multi-step workflows that go beyond AskUserQuestion’s multiple-choice format
• Integrate external approval systems: connect to existing ticketing, workflow, or approval platforms
• Implement domain-specific interactions: create tools tailored to your application’s needs, like code review interfaces or deployment checklists

Custom tools give you full control over the interaction, but require more implementation work than using the built-in canUseTool callback.

Related resources

• Configure permissions: set up permission modes and rules
• Control execution with hooks: run custom code at key points in the agent lifecycle
• TypeScript SDK reference: full canUseTool API documentation