Handle approvals and user input

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.

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:

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)

The callback fires in two cases:

1. Tool needs approval: Claude wants to use a tool that isn't auto-approved by permission rules or modes. 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.

Your callback must return within 60 seconds or Claude will assume the request was denied and try a different approach.

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 isn't auto-approved. Your callback receives two arguments:

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

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.

import asyncio

from claude_agent_sdk import ClaudeAgentOptions, 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 hasattr(message, "result"):
            print(message.result)


asyncio.run(main())

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:

When allowing, pass the tool input (original or modified). When denying, provide a message explaining why. Claude sees this message and may adjust its approach.

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")

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)
• 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

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.

The following steps show how to handle clarifying questions:

Question format

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

Here's an example of the structure you'll receive:

{
  "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
    }
  ]
}

Response format

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

For multi-select questions, join multiple labels with ", ". For free-text input, use the user's custom text directly.

{
  "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

import asyncio

from claude_agent_sdk import ClaudeAgentOptions, 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 hasattr(message, "result"):
            print(message.result)


asyncio.run(main())

Limitations

• 60-second timeout: canUseTool callbacks must return within 60 seconds or Claude will retry with a different approach
• Subagents: AskUserQuestion is not currently available in subagents spawned via the Task 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