result_set_active_datasetSelect the active result dataset by ID so you can inspect fire, smoke, particle, surface, or time-series outputs in the BULC fire-simulation app.
Select the active result dataset by id. Node id shapes: slcf- (slice), pl3d-var- (3D data), s3d- (smoke), bndf- (boundary surface, .bf), prt5- (particles), csv-hrr / csv-devc (time series).
| Name | Required | Description | Default |
|---|---|---|---|
| id | Yes | 데이터셋 id |
Changes observed during successful MCP inspections.
v4.0.0Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=false, idempotentHint=true, destructiveHint=false, so the mutation/idempotence profile is covered without contradiction. The description adds no behavioral context beyond that: it never says what changes after selection (view state, dependent graphs/slices) or what happens if an unknown id is passed.
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 sentences, zero filler. The action is front-loaded and the id taxonomy follows as a compact enumerated reference. Nothing is repeated from the schema or annotations.
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?
With no output schema there is no return-value burden, and annotations cover the safety profile, so the definition is close to sufficient. It is still silent on the downstream effect of switching datasets and on error behavior for invalid ids, which matters for a single-parameter state-setting tool.
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%, but the schema's own description is a bare '데이터셋 id'. The description supplies the real semantic content: the id prefix taxonomy (slcf-, pl3d-var-, s3d-, bndf-, prt5-, csv-hrr/csv-devc) that tells the agent which id strings are valid for which data type. That is genuine added value above the schema baseline.
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?
States a specific verb and resource: 'Select the active result dataset by id.' An agent immediately knows this sets which dataset the result view displays. It does not, however, differentiate itself from nearby result tools such as result_set_particles or results_detect.
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?
Usage is implied (pick which dataset is active before inspecting results) but there is no explicit when-to-use, when-not-to-use, or named alternative. The id-shape list indirectly scopes which inputs are legal, which is the only guidance present.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.