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

Annotations cover safety (readOnly/idempotent/non-destructive), and the description adds real context beyond them: it explains the facet-based implementation, that DPLA retired its collections endpoint, and the built-in vs. user-supplied API key behavior. Slightly short of 5 for not discussing rate limits or result ordering nuances beyond 'most first'.

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?

Purpose is front-loaded and content is substantive, but the parenthetical asides and API-key footnote make it slightly denser than ideal; every sentence still carries usable information.

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?

With no output schema, the description still conveys the return shape ('each with how many matching items it holds'), the key handling, and the example invocation, leaving nothing an agent needs to call it correctly.

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 baseline is 3, but the description adds meaning beyond the schema by explaining how q scopes the list to collections whose items match versus how title matches words in the collection name — clarifying the interplay of the two filters.

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 resource (named collections in DPLA institutions) with what it returns (matching item counts), and clearly distinguishes it from dpla_search_items and dpla_item by scope.

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 says when to use it ('to see how American libraries have organised a subject before pulling records with dpla_search_items({ collection })'), naming the alternative tool and the sequencing. Nothing left to inference.

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