Recommend Model

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

With no annotations, the description must carry the full behavioral burden. It usefully discloses that the tool inspects host hardware locally and classifies the image (photos vs illustrations), but it never states that the operation is side-effect-free, or whether a recommended model must first be fetched via download_model. That ambiguity about a possible write step is a real gap for an unannotated tool.

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?

A single, front-loaded sentence that leads with what is inspected and ends with what is produced. Every clause carries information; the only minor cost is the slightly run-on enumeration of inputs.

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?

An output schema exists, so return values need no explanation, and both parameters are documented. However, for a tool that sits between hardware diagnostics and model download/upscaling siblings, the description does not establish prerequisites or workflow position, leaving the agent to guess the sequence.

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 coverage is 100%, so both parameters are already documented, making 3 the baseline. The description adds meaning to image_path by explaining it is used for photo-vs-illustration classification, but says nothing about how the priority parameter ('balanced', 'speed', 'quality') changes the recommendation.

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 names specific verbs (inspect, recommend) and a concrete resource (model, preset, tile size), plus the evidence it uses (GPU specs, VRAM tier, CPU threads, image characteristics). This separates it from pure-lookup siblings such as list_available_models or get_image_metadata, though the overlap with get_system_diagnostics is not explicitly addressed.

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?

Usage is only implied: the tool clearly belongs at the start of an upscaling workflow, but there is no explicit when-to-use statement, no exclusion of alternatives, and no mention of how it relates to get_system_diagnostics or download_model. The agent must infer the ordering and the routing.

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