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multi_turn_edit

Iteratively refine a figure by providing a list of natural-language editing instructions applied in order to fix CJK labels, adjust layout, or improve details.

Instructions

Iteratively refine a figure through a multi-turn editing session.

Sends multiple editing instructions turn-by-turn to fix CJK labels, adjust layout, or improve details. Each turn builds on the previous result for precise iterative corrections.

instructions: List of natural language editing instructions applied in order. Examples: ["修正標題為「急性冠心症處置流程」", "箭頭改紅色", "加大字體"]

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
max_turnsNo
image_pathYes
instructionsYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior3/5

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

With no annotations, the description carries the burden of disclosing behavior. It mentions that each turn builds on the previous result and instructions are applied in order, which is valuable. However, it omits details like whether the original figure is modified or a new file is produced, limits on turns (max_turns), and error handling.

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 concise and front-loaded, stating the core purpose first, then adding useful details and examples. The parameter explanation is included inline, which is acceptable given the schema lacks descriptions, but it could be tightened.

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

Completeness3/5

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

The description covers the core behavior and gives examples, and an output schema exists so return values need not be detailed. However, it lacks explicit guidance on max_turns semantics, path requirements, and how to compare with single-turn edit_figure, making it only partially complete for an agent.

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

Parameters2/5

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

Schema description coverage is 0%, so the description must explain all parameters. It only explains 'instructions' with examples, while 'image_path' and 'max_turns' are left unspecified. Although 'image_path' is self-explanatory, 'max_turns' is not described at all, leaving a significant gap.

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 the tool's purpose: iteratively refine a figure via multi-turn editing. It uses specific verbs ('refine', 'edit') and identifies the resource ('figure'), while distinguishing from siblings like edit_figure by emphasizing the multi-turn, sequential nature.

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 conveys when to use the tool (for iterative corrections needing multiple dependent edits) and provides concrete examples. It doesn't explicitly name alternatives or state when not to use it, but the multi-turn framing implies a preference over single-turn alternatives.

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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