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loras

Find specific LoRAs by querying name, title, tags, trigger words, or description. Returns matching models to use for targeted image generation.

Instructions

Search available LoRAs by name, title, tags, trigger words or description.

Only call this when the request actually calls for one (a named style, character, or concept) — most generations need no LoRA at all. kind can be "content" or "accelerator"; accelerators change the sampling regime rather than the image, so adopting one means adjusting steps and CFG together.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
kindNo
limitNo
queryNo
verboseNo
base_modelNo
Behavior3/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries the full burden. It discloses the behavior of accelerators vs. content LoRAs, noting that accelerators change the sampling regime. It does not mention whether the operation is read-only (though 'Search' implies it) or what the response format is. This leaves moderate gaps, so a 3 is fair.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is two sentences and efficiently conveys the core purpose and usage. It front-loads the action and then adds contextual guidance. It is appropriately sized, though it could benefit from a bulleted list for parameters, but as-is it's concise and clear. Score 4.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The tool has 5 parameters and no output schema. The description does not explain query, limit, verbose, or base_model, nor does it describe the response. It also assumes knowledge of what 'content' vs 'accelerator' means beyond the brief note. Overall, it leaves too much unspecified for a complete tool definition. Score 2.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, so the description must compensate. It explains the 'kind' parameter in detail but ignores query, limit, verbose, and base_model entirely. This is insufficient for a 5-parameter tool, so a 2 is warranted.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's function: 'Search available LoRAs by name, title, tags, trigger words or description.' It identifies the resource (LoRAs) and the action (search). However, it doesn't explicitly differentiate from sibling lora_info, though the search vs. info distinction is inferable. So a 4 is appropriate.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides explicit guidance: 'Only call this when the request actually calls for one (a named style, character, or concept) — most generations need no LoRA at all.' This clearly indicates when to use and when not to, and also explains the kind parameter's role in choosing content vs. accelerator. It doesn't name alternative tools, but the guidance is decisive enough for a 5.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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