Remove all nodes and edges drawn by a specific AI or session, including edges from other layers, while preserving findings and enabling rollback via auto-saved snapshot.
특정 AI/세션이 그린 노드/엣지를 삭제한다.
삭제되는 노드에 걸린 엣지는 다른 layer 것이라도 함께 사라진다. findings 는 남는다. 실행 직전 자동 스냅샷이 저장되므로 응답의 snapshot_id 로 되돌릴 수 있다.
| Name | Required | Description | Default |
|---|---|---|---|
| layer | Yes |
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Changes observed during successful MCP inspections.
v0.0.0Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Even though destructiveHint=true already signals destructiveness, the description adds important behavioral details: edges connected to deleted nodes are removed even if they belong to other layers, findings are preserved, and an automatic snapshot is saved before execution, allowing rollback via snapshot_id. This goes well beyond the annotation.
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 and well-structured: the primary action comes first, followed by important side effects and recovery behavior. Every sentence adds useful information without redundancy.
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 tool with a single required parameter, an output schema, and a destructiveHint annotation, the description covers the essential operational details: what gets deleted, cascading edge removal, preservation of findings, and automatic snapshot-based rollback. An agent has enough context to invoke it correctly.
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?
The input schema only defines 'layer' as a string with no description, and schema description coverage is 0%. The description compensates by explaining that layer refers to the drawing context of a specific AI/session, which gives the parameter meaningful semantics. It could add more detail on how to obtain or format the layer identifier, but the core meaning is clear.
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 clearly states a specific action ('deletes nodes/edges drawn by a specific AI/session') and identifies the resource scope ('layer'). It also distinguishes itself from broader or narrower tools like clear_all, delete_node, and delete_edge by describing how connected edges and findings are handled.
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?
The description establishes clear usage context: use this tool to remove nodes/edges belonging to a particular AI/session layer. It does not explicitly name alternatives or state when not to use it, but the layer-based scope is clear enough for an agent to select it appropriately among siblings.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.