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

Annotations already declare a safe, idempotent, closed-world read, so the description doesn't need to reargue safety. It adds real behavioral context beyond them: results are limited to tracked machines, filtered by a 4-bit memory-fit rule, sorted cheapest-first, and include measured tok/s only where a result has been published.

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 tight sentence with the important scoping (tracked machines, fit rule) front-loaded and ordering stated before the speed caveat. The nested parentheticals make it dense, though no sentence is wasted.

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?

For a read-only lookup with no output schema and fully documented parameters, the description covers the essentials: filtering, ranking, and what the speed figure represents. It could say more about what each returned machine record contains, but nothing critical to invoking it correctly is missing.

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 model, region, max_price, measured_only and limit are already documented in the schema. The description adds only a small amount of meaning beyond that (the model id can be a name, and ids come from list_models); it does not clarify the currency behavior of max_price or the implicit-cap behavior of limit beyond what the schema says.

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?

Names a specific resource (tracked machines that can run a given open model), a concrete fit criterion (4-bit weights in memory), and an ordering rule (cheapest first). It also distinguishes itself from list_models by deferring id lookup to that sibling, so an agent can separate the two without opening schemas.

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?

Explicitly routes the agent to list_models when it lacks a model id, which is the main prerequisite. It does not contrast itself with search_machines or get_machine, so the when-not-to-use case is left implicit rather than stated.

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