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ChengZiiii

opera-houdini-mcp

by ChengZiiii

save_lesson

Records a resolved Houdini issue as a draft lesson with symptom, fix, category, severity, and affected versions, so repeated occurrences strengthen the lesson and support future diagnosis.

Instructions

把解决一个 Houdini 问题的经验沉淀为 lesson(写入个人库 draft 状态)。

触发时机:agent 在解决一个 Houdini 问题后主动沉淀经验。写入个人库
draft 状态,不立即进入检索索引;同 symptom 再次出现会自动累积
strength(只累积不覆盖)。团队 root 默认只读,写入返回
root_not_writable。沉淀的是 advisory 经验,不替代 verify_hou_api /
get_houdini_help / get_best_practices 与目标 Houdini 版本 live
verification。

主动沉淀工作流(advisory 行为注解,非强制协议):用户完成 HDA / 节点流
/ VEX 工作流后说"沉淀这些知识"时,agent SHALL 依次:get_selection 定位
→ capture_workflow_snapshot 取快照 → 组织为 recipe(用法文档,走
save_recipe)或 lesson(经验,走本工具)→ 写入后向用户汇报写入的
id / root / 状态。

加深与研究方法论(advisory 引导,非强制):用户要求"加深 / 改造 / 优化"
既有沉淀时,agent SHALL 先 search_lessons 定位既有 id,再用
capture_workflow_snapshot(自制 HDA 带 include_hda_internals=True)
重新研究,最后 save_recipe(recipe_id=...) 原地更新(recipe 通道),
**不得新增重复知识**。沉淀目标是原理 / 设计意图 / 为什么(方法论优先),
参数按需收录(用户显式要求除外);正文索引用资产级标识(type_full /
hda 资产全名 + 版本),实例名仅辅助,**禁止本机路径入正文**。

参数说明:
- problem / symptom / fix / category / affected_versions: 必填。
- severity: 必填,取值 low / medium / high / critical。
- verified_versions: 可选;缺省 "unknown"。
- root: 可选 root 名;缺省 personal(唯一可写 root)。

返回:新 lesson → {status:success, lesson_id, lesson_status:"draft",
strength:1, root};同 fingerprint 已存在 → strength 递增且内容保留。
错误为 status=error + error={code,message,details}(非法 severity →
ls_write_error 并列出合法值;只读团队 root → root_not_writable)。

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
fixYes
rootNo
problemYes
symptomYes
categoryYes
severityYes
affected_versionsYes
verified_versionsNo
Behavior5/5

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

No annotations are provided, so the description carries the full burden. It fully discloses key behaviors: writes to draft state, does not immediately enter the retrieval index, strength accumulates without overwriting, team root is read-only, and errors (ls_write_error, root_not_writable) and return formats are described. The advisory workflow with SHALL steps also sets clear expectations for the agent.

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 long but front-loaded with the purpose and each section (trigger, workflow, methodology, parameters, return) is information-dense. Every sentence adds value, though some workflow details could be trimmed, but they are contextually important. It is structured with clear delineations.

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?

This is a complex write-the-lesson tool with no output schema and no annotations. The description is remarkably complete: it covers purpose, trigger, lifecycle, team root permission, advisory workflow, prohibition of duplicate knowledge, parameter defaults, return shapes for success/error, and exclusions. No significant gaps remain.

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 description coverage is 0%, so the description must compensate. It lists required parameters, required severity values (low/medium/high/critical), optional verified_versions defaulting to 'unknown', and root defaulting to 'personal'. However, it does not specify formats for affected_versions or category, and parameter names are self-explanatory but not deeply annotated.

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 opens with a clear verb+resource statement: '把解决一个 Houdini 问题的经验沉淀为 lesson(写入个人库 draft 状态)', which specifies the action, target, and state. It also distinguishes this tool from save_recipe by explicitly stating that recipes go to the recipe channel while lessons go through this tool, and from read_lesson/search_lessons by focusing on writing new lessons.

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 trigger timing ('agent 在解决一个 Houdini 问题后主动沉淀经验'), states when not to use it ('不替代 verify_hou_api / get_houdini_help / get_best_practices'), and gives a concrete alternative workflow: recipes go through save_recipe, and deepening existing lessons uses search_lessons + save_recipe instead of this tool. This is thorough guidance on when to use vs. 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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