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Make another AI short

ai_short_another
Read-only

Free. Treats a finished plan as made and writes a NEW plan from it. Nothing is made or charged.

Args: plan: the finished short's plan. kind: like_this | part2 | new_look | translate | shorter. message: what to change (optional, e.g. the language for translate), max 4000 chars. history: the history from the last answer (optional, max 40 turns).

Returns: The same shape as ai_short_draft.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "plan": "<plan>",
      "kind": "<kind>"
    },
    "name": "ai_short_another"
  }
}

Input Schema

NameRequiredDescriptionDefault
kindYes
planYesThe `plan` from ai_short_draft / ai_short_revise.
historyNoThe conversation so far: pass back the `history` the last answer gave you.
messageNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A3.9/5.0
Behavior4/5

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

The annotations only declare readOnlyHint=true. The description adds valuable context beyond them: it states the call is free, nothing is made or charged, and that only a plan is written, not media. It does not explain how `history` affects the output or any limits, but the cost and no-render disclosure is the important behavioral fact here.

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?

The description is front-loaded with the key fact (free, plan only), then uses compact Args/Returns blocks so an agent can map each parameter quickly. No sentence is filler.

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?

There is no output schema, but "Returns: the same shape as ai_short_draft" tells the agent what to expect, and the prerequisite and history round-trip are explained. Taken with the annotations and schema, an agent has enough to call it 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 description coverage is only 50%, so the description earns credit for documenting each argument: `plan` must come from the prior draft or revise call, `kind` lists the five modes, `message` is the change instruction with a translate example, and `history` is the prior conversation capped at 40 turns. This compensates well for the undocumented fields, though it does not explain how `message` and `kind` interact.

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

Purpose4/5

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

The description names a specific action on a specific resource: it writes a NEW plan from an existing finished plan, and the `kind` enum shows the variants it can produce. It does not explicitly distinguish itself from `ai_short_draft` or `ai_short_revise`, and the phrase "Treats a finished plan as made" is slightly confusing, but an agent can still tell what the tool produces.

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

Usage Guidelines3/5

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

The definition implies a prerequisite — you need the finished short's `plan` and the prior `history` — and it notes the call is free and produces nothing rendered. It never says when to prefer this over `ai_short_revise` or `ai_short_draft`, so the agent must infer that this is the variant-generation path after a completed plan.

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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