Retrieve prerequisite knowledge needed to understand a topic, tracing dependency levels up to five steps. Uses official curriculum codes, with a seed graph as fallback.
指定したトピックを理解するための前提知識を返します。公式コード表に概念依存関係がない場合は、同梱seedの前提知識グラフを補助的に使用します。
| Name | Required | Description | Default |
|---|---|---|---|
| depth | No | 前提知識を何段階たどるか | |
| topic | Yes | 対象トピック |
Changes observed during successful MCP inspections.
v0.2.0Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full behavioral burden. It does reveal one non-obvious trait: the fallback to a bundled seed graph when the primary source is incomplete. But it omits return structure, error behavior for unknown topics, and any performance or data-freshness caveats.
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?
Two compact sentences: the core purpose is front-loaded and the fallback caveat follows. No redundant restatement of the tool name or schema fields.
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 two-parameter read tool with no output schema and no annotations, the description covers the purpose and a notable data-source nuance but leaves the shape of the returned graph and behavior on missing topics unspecified. Adequate but with clear gaps.
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 description coverage is 100%, so the two parameters (topic, depth) are already documented in the schema, including the depth range and default. The description adds no syntax, format, or example beyond what the schema provides, making the baseline 3 appropriate.
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 states a specific verb and resource: it returns ('返します') prerequisite knowledge ('前提知識') for a named topic. The purpose is unambiguous and the sibling names (classify_knowledge_scope, search_curriculum, etc.) are functionally distinct, but the description does not explicitly differentiate itself from them.
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 discloses a data-source fallback condition – when the official code table lacks concept dependencies, the bundled seed graph is used supplementarily – which implies usage context. However, it never states when to prefer this tool over siblings like search_curriculum or classify_knowledge_scope, nor any prerequisites or exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.