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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, and the description's 'Read' aligns with these. The description adds valuable behavioral context beyond annotations: it discloses that figures may not apply to nonresident students and that missing figures remain unknown, which helps the agent set expectations about data completeness and applicability.

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?

The description is tight and front-loaded: the first sentence states the core purpose, and the following sentences each add essential caveats without redundancy. Every sentence earns its place, and the length is appropriate for the tool's complexity.

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 a single fully-described parameter and an output schema present, the description does not need to explain return values. It covers the essential context: historical scope, non-applicability caveats, and data-completeness behavior. The tool is simple, and nothing needed for correct invocation 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?

The sole parameter unitid is fully described in the schema as 'IPEDS institution ID from search results,' so schema coverage is 100%. The description adds no additional parameter-level detail beyond referring to 'a college,' so the schema carries the semantic burden.

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 clearly states a specific verb and resource: 'Read historical average net price, income brackets, grants and debt for a college.' This distinguishes it from search_universities by focusing on financial aid data, but it does not explicitly contrast itself with get_university_details, so sibling differentiation is only implicit.

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?

The description provides strong contextual guidance by warning that cohort statistics are not a personalized cost estimate or aid offer, and by advising verification via the official net-price calculator before affordability decisions. However, it does not explicitly say when to prefer this tool over get_university_details or search_universities, so the when-to-use advice is implicit rather than directly routed.

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