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Handle approvals and user input

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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.

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 Python theme={null} 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) ```

```typescript TypeScript theme={null} 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 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.

To automatically allow or deny tools without prompting users, use hooks instead. Hooks execute before canUseTool and can allow, deny, or modify requests based on your own logic. 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 isn't auto-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 Python theme={null} 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()) ```

```typescript TypeScript theme={null} 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 { 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 and a PreToolUse hook that returns {"continue_": True} to keep the stream open. Without this hook, the stream closes before the permission callback can be invoked.

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

```python Python theme={null} 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") ```

```typescript TypeScript theme={null} // 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:

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

<CodeGroup>
  ```python Python theme={null}
  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")
  ```

  ```typescript TypeScript theme={null}
  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" };
  };
  ```
</CodeGroup>

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.

<CodeGroup>
  ```python Python theme={null}
  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)
  ```

  ```typescript TypeScript theme={null}
  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 };
  };
  ```
</CodeGroup>

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.

<CodeGroup>
  ```python Python theme={null}
  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)
  ```

  ```typescript TypeScript theme={null}
  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 };
  };
  ```
</CodeGroup>

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.

<CodeGroup>
  ```python Python theme={null}
  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)
  ```

  ```typescript TypeScript theme={null}
  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 };
  };
  ```
</CodeGroup>

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:

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:

<CodeGroup>
  ```python Python theme={null}
  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)
  ```

  ```typescript TypeScript theme={null}
  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);
  }
  ```
</CodeGroup>

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

<CodeGroup>
  ```python Python theme={null}
  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)
  ```

  ```typescript TypeScript theme={null}
  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);
  };
  ```
</CodeGroup>

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):

```json theme={null}
{
  "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](#question-format) for full field descriptions.

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.

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, join multiple labels with `", "`. If you [support free-text input](#support-free-text-input), use the user's custom text as the value.

<CodeGroup>
  ```python Python theme={null}
  return PermissionResultAllow(
      updated_input={
          "questions": input_data.get("questions", []),
          "answers": {
              "How should I format the output?": "Summary",
              "Which sections should I include?": "Introduction, Conclusion",
          },
      }
  )
  ```

  ```typescript TypeScript theme={null}
  return {
    behavior: "allow",
    updatedInput: {
      questions: input.questions,
      answers: {
        "How should I format the output?": "Summary",
        "Which sections should I include?": "Introduction, Conclusion"
      }
    }
  };
  ```
</CodeGroup>

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:

```json theme={null} { "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.

```typescript theme={null}
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:

```json theme={null} { "label": "Compact", "description": "Title and metric value only", "preview": "

Active users
1,284
" }


### 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       |

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

```json theme={null}
{
  "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:

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

```python Python theme={null} 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()) ```

```typescript TypeScript theme={null} 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 { 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 = {};

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

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:

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:

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