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

Annotations already declare readOnly, idempotent, non-destructive, closed-world, so the safety profile is covered. The description adds genuinely non-obvious context: a hidden base cohort (US four-year, 3,000+ full-time students) that constrains every query, plus exactly what each returned row contains. It does not address pagination limits or what happens when filters yield nothing, but it meaningfully exceeds the annotation baseline.

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 dense sentences: the first front-loads the population, filters and ranking vocabulary; the second enumerates returned fields. Every clause carries information, though the long comma-chained filter list is heavy and sacrifices scannability for completeness.

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 11 optional parameters and no output schema, the description supplies the missing return-value detail (fall 2024 enrollment, beds, bed gap, housing charge, campus link) and the implicit cohort floor. The remaining gap is the tension between the stated 3,000+ floor and a min_fulltime minimum of 0, which is never reconciled.

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 a semantic constraint absent from the schema: the population is pre-filtered to 3,000+ full-time students at four-year US institutions, which interacts with the min_fulltime parameter (min 0) and prevents misreading that parameter as removing the cohort floor. It also documents the output row fields, which no output schema provides.

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 (Lists) and a well-scoped resource: US four-year campuses with 3,000+ full-time students, enumerating both filter dimensions and ranking dimensions. The listing behavior is clearly distinct from get_campus's single-record lookup, but the description never names that sibling, so differentiation is left to inference.

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 filter and ranking vocabulary — an agent can infer this is the tool for discovering/ranking many campuses, with get_campus for one. There is no explicit 'use this when' statement, no mention of alternatives such as get_campus, and no guidance on defaults (sort=bed_gap) or when a filter combination is meaningful.

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