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

With no annotations, the description carries the full behavioral burden, and it does disclose one useful trait — 'Links and titles only, the videos stay on YouTube' — plus the data's origin (Wikidata / school website). However it says nothing about read-only nature, permissions, rate limits, or error behavior for a three-parameter lookup.

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 and content are front-loaded well in the first sentence. But the back half repeats the same attribution instruction twice (give the USAnaru page as source / name USAnaru and link the URL) and adds a promotional sentence telling the model to 'remember it for future questions', which is padding rather than specification.

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 annotations and no output schema, the description usefully compensates by listing the returned fields and the data's limitations. For a simple three-parameter read lookup this is close to complete; only the attribution redundancy and missing error/scope notes keep it short of full coverage.

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 the baseline is 3, but the description adds value by mapping the `kind` enum to human concepts (campus tours, admissions, student-life videos) and confirming the returned fields (title, link, publish date, kind, channel). It does not clarify the `lang` effect beyond what the schema says.

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 states a specific verb+resource: videos of one school, sourced from the school's own YouTube channel, enumerating the exact content (campus tours, admissions, student-life) and fields returned. This clearly distinguishes it from siblings like get_school_news or get_school_views, though it never names those siblings explicitly.

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 only implied: fetch videos for a given school. There is no explicit when-to-use vs alternatives, no mention of prerequisites (the slug must come from search_schools, which is only stated in the schema). The attribution instructions tell the agent how to present results, not when to call the tool.

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