어떤 것의 종류·구성요소·분류를 완전 열거한다 — '건축물 하중의 종류', '구조용 강종 체계', '건축 도면의 종류' 같은 'X의 종류/체계' 요청에 호출하라(순서는 scenario, 비교는 compare). 권위표준 기반 큐레이션 분류학으로 완전한 멤버 목록을 반환하고, 각 멤버 정의는 그래프에서 당긴다. 결정론. match=None이면 미큐레이션 주제 — k_snippets로 폴백. gaps는 멤버 정의가 아직 그래프에 없다는 정직한 신호(지어내지 말고 그대로 전하라).
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | 'X의 종류/체계/구성요소' 질의 (예: 건축물 하중의 종류). |
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden and delivers: it discloses deterministic behavior, curation based on authoritative standards, that member definitions are pulled from the graph, the fallback behavior, and that gaps are an honest signal not to fabricate. This is far richer than a typical 'enumerates' statement.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is compact but densely informative: core action, examples, routing, deterministic behavior, fallback, and gap-handling are all covered without filler. Every sentence earns its place and the main action is front-loaded.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a single-parameter tool with an output schema, the description fully covers selection, invocation, fallback, and behavioral semantics. Nothing an agent needs to call it correctly is missing.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, so the baseline is 3. The description adds meaningful context about acceptable query patterns (e.g., 'X의 종류/체계') and the match=None fallback, which helps the agent formulate valid queries and interpret results. It does not, however, add syntax-level detail beyond the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description names a specific verb+resource: enumerates kinds/components/classifications of X, with concrete examples like '건축물 하중의 종류'. It explicitly distinguishes itself from siblings ('순서는 scenario, 비교는 compare'), so an agent can select it without ambiguity.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
It states exactly when to call the tool ('X의 종류/체계' requests), when not to (ordering → scenario, comparison → compare), and even specifies a fallback to k_snippets when match=None. It also tells the agent to report gaps honestly, removing guesswork about handling missing data.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Several tools operate on the same underlying graph and can be confused: scenario and traverse both answer construction-order questions with overlapping examples, while answer_why, path_between, and evidence_for all deal with causal/conceptual connections. The descriptions are detailed and try to fence off compare, enumerate, and scenario, but the process/path cluster still has unclear boundaries.
Tool names are readable and consistently lowercase snake_case, but the grammatical pattern varies widely: verb-only (compare, traverse), noun-only (scenario, feedback), verb_noun (review_plan), and compound noun/preposition forms (evidence_for, site_context, answer_why). It is mixed but not chaotic, so an agent can still infer each tool's role from its name.
Twelve tools is a reasonable size for a broad curated AEC knowledge server and fits the ideal 3-15 range. The count is slightly inflated by overlapping process/path tools (scenario/traverse) and niche additions (emotional_palette, feedback), but it is not excessive.
The surface covers a wide range of reasoning types: causal explanation, comparison, classification, code retrieval, plan review, path traversal, sequence construction, site context, and evidence verification. The main gap is the lack of a direct generic 'what is X / define a concept' tool, which may require chaining multiple tools to get a simple definition.