Skip to content

README.md

Latest commit

 

History

History
58 lines (33 loc) · 3.3 KB

README.md

File metadata and controls

58 lines (33 loc) · 3.3 KB
Raw

@deepseek-ai/dsh-tool-ask-user

English | 中文

Model-facing ask_user_question tool over ctx.userQuestions. It lets the model ask the human a concise question when it needs confirmation, a choice, or missing information before continuing.

Tool

ask_user_question accepts:

  • questions — required non-empty array of question objects.
  • id — required stable id on each question, echoed in the answer.
  • question — required question text for each question.
  • header — optional short heading.
  • options — optional choices with label and description. If recommending a choice, put it first and append (Recommended) to that label.
  • multi_select — whether that question may return more than one selected option.

The tool calls ctx.userQuestions.ask() and returns canonical { answers: [{ id, selected, custom? }] }. selected contains option labels; custom carries a free-form answer, supplementing selected for a multi-select question and overriding it for a single-select question. The Native renderer preserves the compact JSON text shape { "answers": [{ "id": "...", "selected": ["..."], "custom": "..." }] }.

Role

This is the Consumer package for the user-questions seam. It does not render UI and does not know how input is collected; it only translates model arguments into AskUserQuestionRequest and returns the human answer to the agent loop.

Model Experience

Tool schema

What the model sees

The model sees the generated ask_user_question schema, including question ids, prompts, headings, options, and multi-select flags.

Token effect

Fixed schema cost on every request where the tool is visible.

KV Cache effect

Prefix-stable while the definition and visibility are unchanged. Plugin lifecycle or scoped restrictions may invalidate reuse from this schema.

Tool-call history and result

What the model sees

The model's full questions remain in the assistant tool-call arguments. After the human answers, the next step sees compact JSON in the exact shape {"answers":[{"id":"<id>","selected":["<label>"],"custom":"<text>"}]}; custom is omitted when unused and selected can contain zero, one, or several labels. UI interaction while the call is pending is not model context.

Token effect

Arguments and answer JSON are data-dependent retained tokens; there is no token cost while waiting for the human.

KV Cache effect

Append-only; newly visible content follows the reusable request prefix and does not invalidate existing KV-cache entries.

Known Limitations and Deferred Work

  • A pending question blocks the tool call until the human answers — the tool declares no timeout-policy budget; cancellation rides the turn's exec.signal only.
  • Runtime-owned subagents cannot ask the user — ask_user_question rejects a live child owned by another agent with DELEGATED_CALLER; the child must include the unresolved question or decision in its final result. Durable lineage does not decide this boundary, so a lineage-bearing session resumed as a runtime root may ask normally.
  • Native answers render as JSON text — the canonical value remains structured, but the model-facing result uses compact JSON rather than a richer content-block vocabulary.