check_hardware_fit

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

No annotations are provided, so the description must carry the burden of behavioral disclosure. It does disclose that this is an estimate, not a guarantee, and explains the calculation basis (parameter count, bytes-per-parameter, runtime overhead). It does not mention what happens on error (e.g., unknown model ID), network dependencies, or whether it performs any network calls. Given the non-destructive, read-only nature implied by 'estimate,' the description is adequate but could be richer.

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

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

The description is front-loaded with the core purpose and then provides an Args section with per-parameter explanations. It is concise, with no fluff, and each sentence earns its place. The only minor redundancy is repeating the purpose in the Args section (the 'auto' description), but overall it's well-structured and appropriately sized.

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

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

The tool has three parameters, an output schema, and a moderately complex estimation logic. The description covers the estimation approach, the parameters, and the caveats. It does not mention what happens with invalid input or whether it requires network access, but given the presence of an output schema (which defines return values) and the clear parameter semantics, the description is complete enough for an agent to call the tool correctly. The missing details are edge-case behaviors, not core usage.

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

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

Schema description coverage is 0%, but the description compensates by providing detailed semantics for each parameter: model_id with a concrete example, available_vram_gb with unit and example, and precision with a clear explanation of each option including the meaning of 'auto'. This goes beyond the schema's titles and enums, giving an agent precise guidance on what values to supply and what they do.

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

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

The description clearly states the tool estimates whether a model fits in a given VRAM amount and at which precision. It uses a specific verb (estimate/check) and resource (hardware fit) and distinguishes it from sibling tools like search_models or compare_models by focusing on VRAM feasibility. The purpose is unambiguous and actionable.

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

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

The description explicitly frames the tool as an estimator and cautions that actual usage varies by framework, batch size, and context length. It does not name alternatives or state when to avoid this tool, but the context is clear enough for an agent to infer when it's appropriate: when reasoning about hardware constraints. A slight gap is the lack of explicit 'use this vs. that' routing, but the distinctive purpose covers most of it.

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