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

Annotations already declare this a safe, read-only, idempotent, closed-world operation, so the bar is lower. The description still earns credit by disclosing a real behavioral trait beyond them: prices are refreshed only once per day, so results may be stale, and it lists the exact metrics returned (price, tok/s, value, check date, URL).

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?

Two sentences, front-loaded with purpose and immediately followed by return contents and the freshness caveat. Nothing is repeated and nothing 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 an eight-parameter, all-optional search with no output schema, the definition covers purpose, returned fields, and data-freshness limits adequately. It stops short of explaining result ordering behavior or what an empty result means, which would have made it fully self-contained.

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 100% and the enums already document defaults and meanings for kind, sort, region and benchmark. The description only restates the same axes (budget, memory, type) without adding units, edge cases, or interaction notes between parameters such as max_price and sort=price, so baseline 3 applies.

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?

States a specific verb and resource ('Find local AI computers and graphics cards') plus the three filtering axes (budget, memory, type), and enumerates what is returned. It does not differentiate itself from the closest sibling, find_for_model, so an agent must infer the split between them.

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 implied by the filtering dimensions ('by budget, memory and type'), which tells the agent this is a discovery/browse tool rather than a lookup. There is no explicit when-to-use statement, no mention of when to prefer find_for_model or get_machine, and no stated prerequisites.

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