recommend_local_models

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

With no annotations, the description carries the full behavioral burden and does well: it discloses that fit flags are computed for local GPUs, explains the three fit categories, notes that newest Hub IDs are marked, and states that nothing is hidden. It intentionally reveals the hardware-dependent filtering behavior, especially the CPU-only fallback.

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?

Three sentences, all dense with useful information and zero fluff. The most important point — exhaustive catalog with fit flags — is front-loaded, and the extra details about version selection and accelerator absence are compact.

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?

Despite having no output schema and no annotations, the description gives enough context for an agent to know what to expect: fit categories, version preference marks, exhaustive coverage, and CPU-only behavior on machines without an accelerator. It could add a sentence about the intended follow-up (e.g., download or start), but it is already quite complete for a zero-parameter read-style tool.

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?

The tool has zero parameters and an empty input schema, so there is no parameter burden for the description to carry. The baseline for no-parameter tools applies, and the description adds no unnecessary param-related filler.

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 that the tool surfaces every catalog model for local vLLM with hardware-fit flags, which conveys the resource and the kind of recommendation being made. It does not explicitly contrast itself with sibling list_local_models, but the scope ('Every catalog model' and 'fit flags') is sufficiently specific.

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 implies the tool should be used when selecting models for this computer's GPUs and when the user wants an exhaustive, unfiltered view of the catalog. However, it does not explicitly say when not to use it or mention alternatives like list_local_models for installed models.

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