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execute_action_chain

Execute multiple UI actions (click, fill, select, press) in one call, then observe the result once. Includes fallback locators and error handling for reliable automation.

Instructions

批量动作链:一次调用顺序执行 click/fill/select_option/press 多个动作,最后统一观察一次并返回 observation(浮层/消息/跳转)。actions 每项 {action, by, selector, iframe_selector, value, click_type, input_method, clear_first, press_enter, key, description};stop_on_error=True 遇错即停(默认),False 收集 failed 继续。用于减少 Agent 往返。降级容错:每项可选 fallbacks: [{完整动作参数}] 配置备用定位,主定位失败时按序尝试;执行器还会自动附加兜底变体(antd 常驻 dropdown 的 li[title=...] 选项自动补/去 >> nth=N 变体、role↔css 互退),全部失败才中断,错误信息含已尝试的定位方案数。生成脚本时为易歧义动作(下拉选项点击、弹层按钮)配置 fallbacks 可显著提高整链成功率。

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
detailNobrief
actionsYes
visualizeNo
stop_on_errorNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior5/5

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

With no annotations, the description carries full weight and does an excellent job. It discloses sequential execution, single observation at the end, stop_on_error default and behavior, fallback attempt ordering, automatic role/css/list-item variant appending, and that error messages include number of attempted locators. This is rich behavioral detail beyond the schema.

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 information-rich, with every sentence adding value. It is somewhat long but well-structured, proceeding from core function to failure handling to fallback advice. Slightly verbose but justified by the tool's complexity.

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?

Given the complex multi-action nature of the tool, minimal annotations, and non-descriptive schema, the description covers essential context: execution order, return value, error handling, and fallback behavior. It doesn't explain output schema details, but mentions observation return type, which is sufficient. Overall it's complete enough for an agent to decide and invoke 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 0%, but description compensates by enumerating fields within each action (action, by, selector, iframe_selector, value, click_type, input_method, clear_first, press_enter, key, description) and explaining stop_on_error and fallbacks. It doesn't describe every parameter exhaustively, but provides enough meaning for an AI agent to construct valid calls.

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?

Description clearly states it's a batch action chain executing click/fill/select_option/press sequentially in one call, which distinguishes it from individual action tools like click_interact and fill_input. It doesn't explicitly name sibling alternatives but conveys the batch-execution purpose and return behavior.

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 states '用于减少 Agent 往返' (to reduce agent round-trips), which indicates when to use it instead of multiple individual calls. It also explains fallback usage for ambiguous actions, giving practical guidance for when to configure fallbacks, though it doesn't explicitly say when not to use it.

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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