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list_supported_models

List HuggingFace models supported for fine-tuning on Tuning Engines. Filter by tuning agent to identify compatible models for your task.

Instructions

List the supported base HuggingFace models available for fine-tuning on Tuning Engines. Optionally filter by agent to see only compatible models.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
agentNoFilter models compatible with this agent (e.g. 'code_repo', 'sera_code_repo'). Omit to see all models.
Behavior3/5

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

With no annotations, the description correctly implies a read-only operation via 'List', but does not elaborate on any other behavior such as pagination or rate limits. Adequate for a simple list tool.

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?

Two sentences, no wasted words, front-loaded with purpose. Excellent conciseness.

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?

The description covers the core functionality and optional filter, but does not mention return format or any output details. Still sufficient for selection given no output schema.

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%, and the description adds the detail 'to see only compatible models', which provides more context than the schema description alone.

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 verb 'List', the resource 'supported base HuggingFace models', and the context 'for fine-tuning on Tuning Engines', distinguishing it from siblings like list_models or list_catalog_models.

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

Usage Guidelines4/5

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

The description mentions optional filtering by agent, providing context on when to use the parameter, but does not specify when not to use this tool or compare it to alternatives like list_models.

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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