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Reply in the layout-debug window

reply_in_window

Send a chat message to the layout-debug window after handling a user request, so the watching user learns what changed, in which files, and what is left. If the edit failed, state why.

Instructions

Write into the chat of the layout-debug window. Call it after handling a message from wait_for_message or pending_requests: the user is watching the window, and without this they will not know what you did. Keep it short: what you changed, in which files, what is left. If the edit could not be made, say why and pass status "error". Write in the same language as the user's comment.

Input Schema

NameRequiredDescriptionDefault
roleNoassistant — a reply to the user, system — a service noteassistant
textYesText for the window chat
statusNoWith requestId: "done" when the edit is made, "error" when it could not be madedone
requestIdNoThe requestId of the message you are answering (from wait_for_message or pending_requests). The window ties the reply to that edit and closes it. Omit it for a general note (no edit changes status).

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.2.0

TDQS

A4.5/5.0
Behavior4/5

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

With no annotations, the description carries the full burden and does well: it discloses the user-visible side effect, that omitting it leaves the user uninformed, and that status should be 'error' when the edit fails. It does not cover things like ordering constraints or failure/idempotency behavior beyond the error flag, so it stops short of a 5.

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

Conciseness4/5

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

Dense and front-loaded: purpose first, then when to call, then content guidance, then failure handling. All four sentences earn their place, though the language-instruction sentence is slightly tangential to invocation.

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

Completeness4/5

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

No output schema and no annotations, so the description must stand alone — and it covers purpose, trigger, content, error signaling, and language. Missing only deeper detail on reply threading or timing, which is minor given the tool's narrow scope.

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

Parameters4/5

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

Schema coverage is 100%, so the baseline is 3, but the description adds real guidance: when to use status='error', and the non-schema constraint to write in the user's language. The requestId explanation largely restates the schema description, hence not a 5.

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?

States a specific verb (write) and resource (chat of the layout-debug window), which is clearly distinct from siblings like open_window, wait_for_message, and layout_snapshot. An agent can identify its role without opening a schema.

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 names the trigger: 'Call it after handling a message from wait_for_message or pending_requests', citing two sibling tools by name. It also gives the reason (the user is watching and won't otherwise know what happened), leaving nothing to inference.

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