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make_variants

Generate variant math problems from a source problem using image, text, or question ID. Produces a mother card and variant set for practice.

Instructions

举一反三·母题轮:读图解题打标 → 出母题卡(LLM 轮 ~60s)。图驱动 + D8 渲图旁路。

入参三选一(image_url > stem_text > question_id 优先级):

  • image_url : 公网可达图片 URL(.png/.jpg/.jpeg/.webp)——直接喂入口。

  • stem_text : 纯文本题干(可含 $LaTeX$/markdown)→ MCP 确定性渲图→传 OSS→喂引擎(rendered_stem:true)。

  • question_id: 题库题 id(字符串)→ 先查 biz_question_image 的 oss_url; 无图则走渲图旁路(取该题 stemText 渲图→传 OSS),返回 rendered_stem:true。

  • hint : 追加指令(如「侧重折叠」),默认「帮我把这道题举一反三」。

  • count : 变式数(进 message 文案,实际生成在 generate_variants)。

  • thread_id : 续跑同一母题会话用;缺省自动生成(uuid4)。返回值里带回,后续工具必传它。 返回: {ok, thread_id, status:"ready"|"need_confirm", mother_card, kg_candidates?, reply, rendered_stem?}。

  • rendered_stem=true ⇒ 母题图由渲图旁路生成(题干确定性渲染,opus 读图 OCR)。

  • status=need_confirm(低置信/骨架空分支)→ 读 kg_candidates 挑章 → confirm_variant_chapter。

  • status=ready → 直接 generate_variants。

  • mother_card=None(入口回催图/催登录)→ ok:false,hint=引擎回文(reply)。

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
hintNo
countNo
image_urlNo
stem_textNo
thread_idNo
question_idNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior4/5

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

No annotations provided, so the description carries full burden. It explains the creation workflow, timing (~60s), rendering bypass, error states (mother_card=None), and next steps. It lacks explicit mention of destructive behavior but implies creation only. Could clarify authorization needs.

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 well-structured with bullet points and clear sections. It is somewhat lengthy but each sentence adds value given the tool's complexity. Could be slightly more concise, but not excessively verbose.

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 the tool's complexity, the output schema exists, and the description thoroughly covers return fields (status, mother_card, kg_candidates, etc.) and their meanings. It also explains the rendering bypass and thread_id persistence. Complete for agent use.

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?

Schema coverage is 0%, but the description compensates fully. It explains each parameter's purpose, format, default, and behavior (e.g., priority, auto-generation of thread_id, hint default). This is essential for correct invocation.

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: generating variant questions (mother card) from an image, text, or question ID. It uses specific verbs like '出母题卡' and distinguishes from siblings like generate_variants and confirm_variant_chapter.

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 explicit guidance on when to use each input method with priority order, how to proceed based on status (need_confirm vs ready), and the role of thread_id for continuation. It also references sibling tools for follow-up actions.

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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