Explains a plant preset's L-system grammar, including axiom, productions, turtle interpretation, and growth behavior, to clarify how the preset generates its shapes.
Get a human-readable explanation of a plant preset's L-system grammar: axiom, productions, turtle interpretation, and growth behavior.
| Name | Required | Description | Default |
|---|---|---|---|
| preset | Yes | Preset ID to explain. | |
| showDerivation | No | If true, show the first 3 iterations of derivation. |
Changes observed during successful MCP inspections.
v0.5.0Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are supplied, so the description carries the burden. 'Get' implies a non-mutating read, and the enumeration of returned sections (axiom, productions, turtle interpretation, growth behavior) is genuinely useful behavioral disclosure. However, it does not state whether an unknown/absent preset errors out, whether output is text only, or any cost/latency characteristics — meaningful gaps for a tool with zero annotation coverage.
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?
A single sentence that front-loads the purpose and then itemizes the output sections. There is no filler, hedging, or 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?
With no output schema, the description compensates well by naming the sections of the explanation the caller will receive, which is the key missing piece for an explain/read tool. It falls short only on error/edge-case behavior, which is a minor omission for a two-parameter read operation.
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%, with both 'preset' and 'showDerivation' documented in the schema itself, so the baseline is 3. The description adds only the notion that 'preset' refers to a plant preset with an L-system grammar, and says nothing extra about what showDerivation changes in the returned explanation.
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 uses a specific verb ('Get a human-readable explanation') plus a precisely scoped resource ('a plant preset's L-system grammar') and enumerates the contents (axiom, productions, turtle interpretation, growth behavior). It is clearly a read/explain counterpart to write-side siblings like design_set_plant_grammar, though it never names a sibling to reinforce the distinction.
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 says nothing about when to call this versus design_set_plant_grammar, design_grow_plant, or design_list_plant_presets, and gives no preconditions (e.g. that the preset ID must already exist). The usage is only faintly implied by the word 'Get'; there are no explicit when/when-not cues.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.