Lists 110 plant presets and filters them by category, tags, complexity, or engine type to help select generative plant designs.
List all 110 plant presets, optionally filtered by category, tags, complexity, or engine type.
| Name | Required | Description | Default |
|---|---|---|---|
| tags | No | Filter by tags (e.g. ['tropical', 'evergreen']). | |
| engine | No | Filter by engine type. | |
| category | No | Filter by botanical category. | |
| complexity | No | Filter by complexity tier. |
Changes observed during successful MCP inspections.
v0.5.0Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations exist, so the description carries the full burden; 'List' correctly signals a non-mutating read and the count of 110 sets expectations about result size. However, it says nothing about whether filters combine with AND or OR, whether results are paginated or capped, or what fields each preset returns — meaningful gaps for a tool with no 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 verb, resource, and total count, then lists the filters in one clause. Nothing is padded or repeated.
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 zero-required-parameter read tool with full schema coverage and no output schema, the description is nearly sufficient: it conveys scope, count, and filtering options. The remaining shortfall is the absence of any hint about return shape or filter-combination behavior, which an agent would still have to discover empirically.
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% and three of the four parameters carry enums with documented values, so the schema already does the work. The description merely restates the same four filter names (category, tags, complexity, engine type) without adding combination semantics or example values beyond what the schema provides, so the baseline 3 applies.
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 (List) and resource (plant presets) plus the total cardinality (110), so an agent immediately knows this is a full-enumeration read of preset definitions rather than of plant instances. It does not explicitly distinguish itself from the similarly named preset-listing siblings (design_list_pattern_presets, design_list_terrain_presets) or from design_search_plants, leaving that separation to the tool name alone.
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 phrase 'optionally filtered by' describes the parameters, not when to choose this tool. There is no statement of when to use it versus design_search_plants or design_add_plant, no exclusion conditions, and no prerequisite context, so usage must be inferred entirely from the name.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.