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get_top_checkpoints

Read-onlyIdempotent

Retrieve top-performing checkpoint models for specific AI base models like SDXL or Flux, filtered by download popularity and time period.

Instructions

Get top checkpoint models for a specific base model.

Best for finding SDXL, Flux, Pony, Illustrious checkpoints. Base models: SD 1.5, SDXL 1.0, Flux.1 D, Flux.2 D, Pony, Pony V7, Illustrious, NoobAI, Chroma, HiDream, etc.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
base_modelNoSDXL 1.0
periodNoMonth
sortNoMost Downloaded
limitNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior3/5

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

Annotations already provide readOnlyHint=true, destructiveHint=false, idempotentHint=true, and openWorldHint=true, covering safety and idempotency. The description adds context about what types of checkpoints it's best for (SDXL, Flux, etc.) and example base models, which helps set expectations. However, it doesn't disclose additional behavioral traits like rate limits, authentication needs, or pagination behavior beyond what annotations provide.

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 appropriately sized with two sentences. The first sentence states the core purpose, and the second provides usage context with examples. There's no wasted text, and it's front-loaded with the main functionality. However, the second sentence could be slightly more structured for clarity.

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?

Given the tool's moderate complexity (4 parameters, no required ones), rich annotations (covering safety and idempotency), and the presence of an output schema (which handles return values), the description is reasonably complete. It covers purpose and usage context adequately. The main gap is parameter semantics, but overall it provides sufficient context for an agent to understand when and how to use this tool.

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 schema provides no parameter documentation. The description mentions 'base_model' implicitly by listing examples (SD 1.5, SDXL 1.0, etc.), but doesn't explain the other three parameters (period, sort, limit) at all. It partially compensates for one parameter but leaves three undocumented, failing to adequately address the coverage gap.

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 purpose: 'Get top checkpoint models for a specific base model.' It specifies the verb ('Get') and resource ('top checkpoint models'), and distinguishes it from siblings by focusing on checkpoints rather than images, creators, or other model types. However, it doesn't explicitly differentiate from 'get_top_loras' or '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 Guidelines3/5

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

The description provides implied usage guidance with 'Best for finding SDXL, Flux, Pony, Illustrious checkpoints' and lists example base models. This suggests when to use it (for those specific checkpoint types) but doesn't explicitly state when NOT to use it or name alternatives like 'get_top_loras' for LoRA models or 'search_models' for broader searches. The guidance is helpful but not comprehensive.

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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