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

Annotations already declare readOnlyHint, idempotentHint, and non-destructive behavior. The description adds useful behavioral context: the operation spans the entire collection unless scoped by q, and returns ranked term lists with counts across four dimensions. No contradiction with annotations.

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 with no filler: the first states action and scope, the second gives the use case, the third describes output dimensions. Information is front-loaded and each sentence earns its place.

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 tool with two optional parameters, a complete input schema, and an output schema, the description covers scope, dimensions, use case, and return shape. Defaults and constraints are already in the schema, so no critical context 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 description coverage is 100%, with both q and facet_size already documented including defaults, ranges, and an example. The tool description reinforces the q-scoping semantics but does not add new parameter-level detail beyond the schema, 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?

The description uses a specific verb ('Get') and resource ('top facet counts'), enumerating exactly four dimensions: subject, provider, type, and state. It clearly distinguishes from sibling tools like dpla.archives.search and dpla.archives.detail by focusing on aggregate facet counts rather than records or subject browsing.

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?

Provides a clear use case: discovering which subject areas, contributing libraries, or media types dominate a topic, and explicitly contrasts global vs query-scoped behavior. It does not name alternative sibling tools or state when not to use it, but the intended context is evident.

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