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get-recommendations-models

Filter and list AI image generation models by capabilities, type, tags, and privacy settings to find suitable options for creative projects.

Instructions

List recommended models matching the given filters

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
capabilitiesNoFilter models by capabilities. Multiple values comma-separated. Examples: `txt2img`, `img2img`, `inpaint`, `controlnet`, `txt2img,img2img,2img`. Also accepts prefixes or suffixes values such as `txt2`, `img2`, `inpaint`, `control`. Default: no filter
excludeModelIdsNoExclude specific models by their IDs. Multiple IDs comma-separated. Example: `model1,model2,model3`. Default: no exclusions
limitNoThe maximum number of models to return. Default: `10`, Maximum: `30`
nextTokenNoPagination token to retrieve the next page of results. Use the `nextToken` from the previous response. Default: first page
privacyNoFilter models by privacy level. Default: `private`. Values: `private`, `public`
originalAssetsNoIf set to true, returns the original asset without transformation
typeNoFilter models by type. Examples: `flux.1`, `flux.1-lora`, etc. Default: no filter
tagsNoFilter models by tags. Multiple tags comma-separated. Example: `anime,portrait,style`. Default: no filter
excludeTypesNoExclude models by type. Multiple types comma-separated. Example: `sd-1_5,flux.1`. Default: no exclusions
Behavior2/5

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

With no annotations provided, the description carries full burden but only states it's a listing operation. It doesn't disclose behavioral traits like whether this is a read-only operation (implied but not stated), pagination behavior (only hinted via 'nextToken' in schema), rate limits, authentication requirements, or what 'recommended' means algorithmically. The description adds minimal value beyond the basic action.

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

Conciseness5/5

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

The description is a single, efficient sentence that front-loads the core purpose. Every word earns its place with no redundancy or unnecessary elaboration.

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?

For a tool with 9 parameters, no annotations, and no output schema, the description is inadequate. It doesn't explain what 'recommended' means, how results are ordered, what the output format looks like, or provide any context about the recommendation algorithm. The description leaves too many questions unanswered for a tool of this complexity.

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

Parameters3/5

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

Schema description coverage is 100%, so parameters are fully documented in the schema. The description adds no additional parameter semantics beyond implying filtering occurs. Baseline 3 is appropriate since the schema does all the parameter documentation work.

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 'List recommended models matching the given filters' clearly states the action (list) and resource (recommended models) with a qualifier (matching filters). It distinguishes from generic 'get-models' siblings by specifying 'recommended' models, though it doesn't explicitly contrast with 'get-public-models' or 'post-search-models' which might have overlapping functionality.

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

Usage Guidelines2/5

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

The description provides no guidance on when to use this tool versus alternatives like 'get-models', 'get-public-models', or 'post-search-models'. It mentions 'recommended' but doesn't explain what makes a model recommended or when this tool is preferable over other listing/search tools.

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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