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generate_calc_items

Generate calculation problems and answers as structured data for custom layouts. Returns {q,a} pairs without PDF rendering, ideal for daily practice exercises.

Instructions

只出计算题数据、不渲染 PDF —— 拿 {q,a} 塞进自有版面(每日一练等)。

与 generate_calc_paper 同一套生成器(确定性、跨组去重、难度档、seed 复现), 区别 = 不出 PDF、直接返回题面与答案,排版归 agent。 🔴 题目由程序生成,答案由生成器同步算出——不需要再人工/LLM 验算。 参数: groups: [{type, count, label?, level?}],type 从 list_calc_types 查(严禁编造); level=basic/advanced 出基础版/提高版(同考点不同难度,两版一次做完)。 seed: 随机种子(任意字符串,同串复现同一批题;空=随机)。 fill_rows: 🔴 缺省 False = 要几题给几题(自排版场景不该被静默改数量); True 才向上凑整到栏数倍数。 返回: {ok, total, seed, groups:[{label, cols, mode, items:[{q,a}]}]}。

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
seedNo
groupsYes
fill_rowsNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior5/5

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

Discloses key behavioral traits: deterministic generation via seed, cross-group deduplication, difficulty levels, and that fill_rows flag affects output count. No annotations provided, so description carries full burden and meets it well.

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?

Well-structured: starts with clear purpose, differentiates from sibling, notes important safety info, then details parameters. Every sentence adds value without redundancy. Efficient use of space.

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 3 parameters and no annotations, description covers all necessary context: return structure, parameter constraints, and special cases like level applicability. Output schema not provided but return object is described.

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

Parameters5/5

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

Explains each parameter beyond schema: e.g., type must come from list_calc_types, level is basic/advanced with specific applicability, label is internal only, and fill_rows default False behavior. Adds context not in 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?

Description clearly states the tool outputs calculation data without rendering PDF, contrasting with sibling generate_calc_paper. It specifies the output format {q,a} for custom layout, making its purpose distinct and unambiguous.

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?

Explicitly states when to use this tool vs generate_calc_paper: '不出 PDF、直接返回题面与答案,排版归 agent'. Also warns against manual verification due to automatic answer computation.

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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