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opera-houdini-mcp

by ChengZiiii

search_lessons

Search past Houdini lessons and recipes across all knowledge roots to resolve errors, unknown APIs, or persistent issues. Use when retries fail or you need proven solutions.

Instructions

跨全部可用知识库 root 检索既往经验(published lessons + root recipes)。

触发时机:agent 在 Houdini 操作遇到报错、重试第 2 次仍未解决、或遇到
不认识的 API/参数时,先调用本工具检索既往经验;命中后用 read_lesson
拉全文。本工具是 advisory,不替代 verify_hou_api / get_houdini_help /
get_best_practices,也不替代目标 Houdini 版本的 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 save_lesson)→ 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 先调用本工具定位既有 recipe/lesson 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,**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**(HDA 库路径 / hip 完整
路径,团队知识库跨机器误导源)。

参数说明:
- query: 检索文本(可为空串 → 按新鲜度/priority 基线浏览)。
- category / severity: 精确过滤(severity: low/medium/high/critical)。
- node_type: doc 文本子串过滤(如 /obj/geo1、sop/attribwrangle)。
- houdini_version: affected_versions 子串过滤(如 H21.0)。
- scope: 可选 root 名(如 "personal")或 "all";缺省检索全部 root。

返回统一 envelope:status/query/top_k/matched/returned_count/truncated/
results(紧凑摘要,含 source_root)/draft_suggestions;unavailable root
附 _warning。错误为 status=error + error={code,message,details}
(未知 scope → ls_unknown_root)。整体过 apply_response_cap。

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryYes
scopeNo
categoryNo
severityNo
node_typeNo
houdini_versionNo
Behavior5/5

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

With no annotations provided, the description carries full responsibility for behavioral disclosure. It states the tool is 'advisory', describes the return envelope (status/query/top_k/matched/returned_count/truncated/results/draft_suggestions), error format (status=error + error={code,message,details}), and special behavior for unknown scopes. It also includes detailed workflow annotations, exceeding what annotations would typically provide.

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 clear sections (purpose, trigger, workflows, parameters, return envelope) and front-loaded with the main purpose. It is long, but most content earns its place. The inclusion of the knowledge deposition workflow is arguably tangential to the search tool itself, adding some extra length, but still provides useful context for the agent.

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 has 6 parameters, no output schema, and no annotations, the description is remarkably complete. It covers purpose, trigger conditions, alternatives, parameter semantics, output envelope, error handling, and workflow integration. An agent has everything needed to select and invoke the tool correctly.

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 has 0% description coverage, so the description compensates fully. It explains each of the 6 parameters: query (can be empty for baseline browsing), category/severity (exact filters with severity enum), node_type (substring filter with examples), houdini_version (substring filter), and scope (optional root name or 'all' with default). This adds substantial meaning beyond the schema's bare type declarations.

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 function: '跨全部可用知识库 root 检索既往经验(published lessons + root recipes)', specifying a concrete verb (search) and resource (all knowledge base roots). It further distinguishes itself from siblings by explicitly naming related tools (read_lesson, verify_hou_api, get_houdini_help, get_best_practices) and explaining its advisory role.

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 gives explicit trigger conditions: '遇到报错、重试第 2 次仍未解决、或遇到不认识的 API/参数时,先调用本工具'. It also states when not to use it, saying it does not replace live verification or the help tools. This is a model of when/when-not guidance.

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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