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SabaCan0141

duet-mcp

by SabaCan0141

await_change

await_change

Wait for the next change to a shared JSON document and retrieve the updated doc and revision, with optional filters for operation type or revision to monitor specific edits.

Instructions

省略すると現在の doc と revision を即時取得する。sinceRevision は観測した文字列をそのまま渡す。その版以後のコミットを待ち、既に変更があれば即返す。until は待つ op 名を絞る。全応答に doc が載る。truncated は差分を説明できないという意味。doc を確認すること。再起動や履歴の保持範囲外でも最新 doc を即返す。timedOut の場合も doc と revision を組で読む。changes は対象 op の変更説明で、連続した同じ参加者の同じ op は count にまとまる。activity は最終活動からの経過ミリ秒で、編集完了や優先権は保証しない。activity 自体では起床しない。

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
untilNo待つ op 名。省略すると任意の変更。
timeoutMsNo既定 25000、上限 120000。
sinceRevisionNo直前に観測した revision。省略すると待たずに今の doc を返す。

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.6.1

TDQS

A4.6/5.0
Behavior5/5

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It thoroughly explains response guarantees, such as doc appearing in every response, immediate return even outside history retention, timedOut behavior, changes grouping into count, and the non-guarantees around activity and priority. This is far beyond minimal transparency.

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?

The description is dense and not structured with headings, but nearly every sentence conveys a distinct behavioral or return-value detail that is necessary because no output schema exists. It is front-loaded with the most common use mode, and while a bit wall-of-text-like, it avoids 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?

Given three optional parameters, no output schema, and no annotations, this description does an unusually complete job. It explains return fields, edge cases such as truncated and timedOut, grouping behavior for changes, and the meaning of activity. An agent has enough information to select and invoke the tool correctly.

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. The description adds important semantic guidance beyond the schema: sinceRevision must be passed as an observed string, omitting it returns the current doc immediately, and until narrows the operations waited on. That extra context justifies a 4.

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 that the tool waits for commits after a given revision and returns immediately if changes already exist, or fetches the current doc and revision when sinceRevision is omitted. This specific verb-plus-resource behavior clearly separates it from the write-oriented sibling tools like set_text and set_settings.

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

Usage Guidelines4/5

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

The description gives clear parameter-driven usage context: omit sinceRevision to get the current state, provide it to wait for subsequent commits, and use until to filter which operations to wait for. It does not explicitly discuss when not to use this tool or name alternatives, but the usage context is strong.

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