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LongLiveY96

ArkTS Helper MCP

by LongLiveY96

ask_ai_batch

Submit multiple queries concurrently to reduce total wait time, with parallel execution replacing sequential calls.

Instructions

批量向华为开发者官方智能问答助手提问(并行处理)。

使用场景

当需要同时查询多个问题时使用此工具:

  • 一次调用处理多个相关问题

  • 服务器端并行执行,大幅节省时间

  • 适用于需要查询多个不同主题的场景

与 ask_ai 的区别

  • ask_ai:单次提问,多个问题需要多次调用

  • ask_ai_batch:批量提问,一次调用处理多个问题(并行执行)

使用示例

示例1 - 批量查询不同主题: 调用:ask_ai_batch({ queries: ["Navigation 组件用法", "List 性能优化", "@State 和 @Prop 区别"] })

示例2 - 批量查询相关问题: 调用:ask_ai_batch({ queries: ["如何实现页面跳转", "如何传递参数", "如何返回数据"] })

性能优势

假设单个问题响应时间 60 秒:

  • 串行调用 3 个问题:60s + 60s + 60s = 180 秒

  • 批量并行调用:约 60 秒(取决于最慢的问题)

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queriesYes问题列表,支持中英文。例如:["Navigation 组件用法", "List 性能优化"]
newSessionNo是否开启新会话(默认 false,复用之前的会话上下文)
Behavior4/5

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

No annotations are provided, so the description carries full burden. It discloses parallel execution, session reuse via newSession parameter, and performance comparison. However, it does not mention error handling or rate limits, which would be beneficial.

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 well-structured with clear sections (使用场景, 区别, 示例, 性能优势). Every sentence provides useful information; no fluff. The performance comparison is especially helpful.

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?

For a tool with 2 parameters and no output schema, the description covers usage, alternatives, examples, and performance expectations. It is complete enough for an agent to invoke correctly without ambiguity.

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%, but the description adds value by providing concrete examples for the queries parameter and explaining the newSession parameter's role in session context. This goes beyond the schema descriptions.

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 it is for batch querying the Huawei developer AI assistant in parallel. It explicitly distinguishes from sibling tool ask_ai by describing batch vs single query behavior.

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?

The description provides a dedicated '使用场景' section explaining when to use, contrasts with ask_ai, and gives multiple usage examples. It explicitly states server-side parallel execution and time savings.

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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