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ask_user

Resolve ambiguity or gather user preferences by bundling 1-3 questions into one form. Supports single/multi-select with auto-injected 'Other'.

Instructions

Ask the user 1-3 questions in a single form. Each question has its own options and can be single- or multi-select. Bundle related decisions in ONE call instead of multiple turns.

Use when:

  • The prompt is ambiguous on multiple dimensions (audience + aesthetic + length) — bundle them into one form

  • You need a decision before proceeding (delete existing? which section first?)

  • Multiple valid approaches exist and user preference matters

Returns one of:

  • { answers: [...] } — array indexed to questions order. string for single-select, string[] for multi-select. The string MAY be one of the option labels OR custom text the user typed via the auto-injected "Other..." option.

  • { freeText: "..." } — when the user typed a free-form answer in the chat input instead of submitting the form. Treat as authoritative — user is overriding the structured options.

Each question:

  • question: required prompt string. Self-contained — no separate header/label, the question text IS the heading.

  • options: 2-3 options, each { label, description? }. The form auto-injects an "Other..." row, so the user always sees options.length + 1 rows total — keep options ≤ 3 to stay within the 4-row visual cap.

  • multiSelect: optional boolean (default false). Use only when the answer is genuinely a list (e.g. "which features?"). For mutually exclusive choices keep false.

Conventions:

  • First option = recommended. If you have a strong default for the user, put it FIRST and add "(Recommended)" at the end of the label. The form auto-focuses the first option and the dev/auto-pick fallback selects it — both work better with a deliberate recommendation.

  • Do NOT include an "Other" option yourself — the form auto-injects an "Other..." row per question with an inline text input. Don't add a redundant one.

  • Bundle aggressively. 2 related dimensions in ONE call beats 2 sequential turns.

Example: ask_user({ questions: [ { question: "Who is this for?", options: [{label:"B2B SaaS"},{label:"Consumer"},{label:"Developer tool"}] }, { question: "What visual direction?", options: [{label:"Minimal"},{label:"Bold/Brutalist"},{label:"Neon/Cyber"}] } ]})

Skip when the prompt is already actionable — asking adds a turn and costs momentum.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
questionsYes1-3 questions to present in a single form (3 is the soft cap; bundle related decisions but don't pad).
Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations exist, so the description fully discloses behavior: return format (answers array or freeText), UI behavior (auto-injected 'Other', visual cap, auto-focus), conventions, and recommended option placement.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Well-structured with sections: purpose, usage, return, question details, conventions, example, skip note. Every sentence adds value without redundancy.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Comprehensively covers all aspects given no output schema: return values, parameter nuances, behavioral context, and usage guidelines. Leaves no gap for agent decision-making.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Despite full schema coverage, the description adds critical context: conventions for recommended option, bundling, not adding own 'Other', and detailed return format. Goes well beyond schema explanations.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool asks the user 1-3 questions in a single form. It distinguishes itself from sibling tools like 'describe' or 'help' by focusing on interactive decision-making.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicitly provides when to use (ambiguity, need for decision, multiple valid approaches) and when to skip (prompt already actionable). Includes bundling advice and examples.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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