deepseek-harness/packages/interaction/tool-ask-user

description, kind

description	kind
The model-facing ask_user_question tool over the user-questions seam, for users and maintainers composing or debugging interactive agent surfaces.	package-reference

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

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Summary

dsh-tool-ask-user gives the model one tool — ask_user_question — for asking the human a concise question when it needs confirmation, a choice, or missing information before continuing. The tool pauses until the first scoped answerer accepts the request, then feeds that answer back into the agent loop as an ordinary tool result, so no loop mechanics change. The tool returns the canonical { answers: [...] } shape, rendered as compact JSON text. It renders no UI itself and does not know how input is collected; the Web client contributes its answerer through Remote Events. A runtime-owned child agent cannot ask the user; it must include the unresolved question in its final result.

Table of Contents

• Use this package
• Understand the implementation
• Further Exploration
• Model Experience
• Known Limitations and Deferred Work
• Dev Note

---

Use this package

Compose this plugin wherever the model should be able to pause for a human decision: it provides the ask_user_question tool and needs the ctx.userQuestions seam with an answerer that accepts the scoped request. Without one, the tool call fails with an error instead of degrading.

When to call the tool

The model calls ask_user_question when it needs confirmation, a choice, or missing information before proceeding. Send one or more questions, each with a stable id that is echoed in the answer; a recommended option goes first with (Recommended) appended to its label.

{
  "questions": [
    {
      "id": "cleanup",
      "question": "Proceed with the destructive cleanup?",
      "header": "Confirm",
      "options": [
        { "label": "Yes, delete them (Recommended)", "description": "Removes the three stale files." },
        { "label": "No, keep them", "description": "Aborts the cleanup." }
      ]
    }
  ]
}

What the model gets back

The tool returns one answer object per question: selected holds the chosen option labels, and 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": "cleanup", "selected": ["Yes, delete them (Recommended)"] }] }

When the call fails

The tool call blocks until the human answers and cancels only through the turn's signal. No accepting answerer, an aborted call, or a caller that is not the exact live runtime root each settles as an error the model sees in the tool result — most notably, a live child agent owned by another agent is rejected (DELEGATED_CALLER) and must include the unresolved question or decision in its final result.

---

Understand the implementation

Implementation internals — click to expand

The observable behavior is covered in Use this package; this section explains the tool definition and its relationship to the seam.

Source map

File	Role
src/index.ts	Tool registration: ask_user_question schema, execute path, result render
src/invariant.ts	Invariant companion (no runtime invariant; the seam owns execution relations)

Consumer role

The plugin registers one defineTool entry on ctx.tools with injects ['tools', 'userQuestions']. execute maps model arguments into an AskUserQuestionRequest, forwards the exact calling agent and the turn's signal, and maps the accepted answer back into the canonical answers array. The seam owns identity checks, intent validation, waterfall dispatch, and the error taxonomy; this package only translates.

Result rendering

The render output projects the structured value to a single text block via JSON.stringify, which is why the model-facing result is compact JSON rather than a richer content-block vocabulary.

---

Further Exploration

Read these pages when the package-level contract is not enough. They move from the tool surface to the seam contract and its answerer waterfall.

• User interaction subsystem reference — the service contract, question vocabulary, and answerer waterfall behind this tool.
• Tool catalog — the generated ask_user_question schema.
• user-questions package — the seam this tool consumes.
• Interaction group map — adjacent approval and command surfaces.

---

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

These limits define when the tool is a poor fit. They are current package constraints, not a UI backlog.

• 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.

Dev Note

Working context for maintainers — click to expand

None.