List AI accelerators in the dataset

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

Annotations already declare readOnlyHint=true and openWorldHint=false, so the safe-read profile is covered. The description adds useful dataset context ("curated, source-cited spec dataset"), but says nothing about return format, ordering, or result size for a list operation.

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 with the verb and resource first, and the optional filter last. Tight, though the parenthetical vendor list slightly crowds the core statement.

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 optional single-param list tool with no output schema and read-only annotations, the description covers purpose, data source, and filtering. Only the return shape and pagination behavior remain unspecified, which is a minor gap.

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?

One parameter at 0% schema description coverage, so the description must carry the load. It does explain the vendor filter's purpose and names plausible values (NVIDIA, AMD, Google TPU, AWS Trainium), but does not specify expected casing or exact accepted strings.

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 clear verb (List) and resource (GPUs/AI accelerators in the spec dataset), and scopes the dataset with concrete vendor examples. It does not explicitly distinguish itself from chip_specs or compare_chips, which is the only gap.

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?

"Optionally filtered by vendor" implies when the vendor param matters, but there is no explicit when-to-use or when-not guidance and no routing to alternatives like chip_specs for a single chip or compare_chips for comparisons.

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