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kie_catalog_set

When automatic price matching misses a model, manually add its id, type, credit price, and limits. Use documented prices only.

Instructions

Record a model by hand: id, type, price in credits, limits. Stored separately from live data and applied on top of it — use this only for models the automatic price matching misses. Do NOT invent a price; take it from the documentation.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
typeNoimage | video | audio
modelYesmodel id, e.g. google/nano-banana
notesNoModel specifics, what it is good for
sourceNoURL of the documentation page this came from
creditsNoPrice in credits per unit
maxRefsNoMaximum number of reference images
priceUnitNoper_image | per_second | per_video | per_track
inputSchemaNoSchema of this model's input field
aspectRatiosNoAllowed aspect ratios
maxDurationSecNoMaximum duration for video
Behavior3/5

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

No annotations are provided, so the description carries the full burden. It discloses a key behavioral trait: 'Stored separately from live data and applied on top of it,' which clarifies override semantics. However, it does not mention whether re-recording an existing model overwrites it, persistence details, or any side effects beyond the separate storage. It adds some context but is not fully transparent.

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 two concise sentences, front-loaded with the action ('Record a model by hand') and immediately followed by essential context. Every sentence earns its place; there is no fluff or redundancy.

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

Completeness4/5

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

For a 10-parameter tool with nested objects and no output schema, the description provides key context: the manual override role, separate storage behavior, and pricing rule. It does not elaborate on all parameters, but the full schema covers them. It misses potential details like interaction with catalog_refresh or what the response looks like, but overall for the complexity, it is reasonably complete.

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

Parameters4/5

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

Schema coverage is 100%, so the baseline is 3. The description adds meaning beyond the schema: it explicitly ties 'price' to documentation sourcing ('take it from the documentation') and mentions 'limits' as a general concept, grouping parameters like maxRefs/maxDurationSec. This provides helpful intent for several parameters.

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

Purpose5/5

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

The description clearly states the tool's function: 'Record a model by hand' with specific fields (id, type, price, limits). It distinguishes this tool from siblings by explaining its role as a manual override for models missed by automatic matching, making its purpose unambiguous.

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?

Explicit usage guidance is provided: 'use this only for models the automatic price matching misses.' It also provides a critical rule: 'Do NOT invent a price; take it from the documentation.' This clearly delineates when to use the tool versus relying on automatic processes.

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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