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

With no annotations, the description carries the full behavioral burden. It usefully discloses that there is no score and never will be, but it does not describe the returned structure, what 'what transfers' means operationally, pagination, locale effects, or any side effects. This is a meaningful gap for a read tool with zero annotation support.

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 short sentences with no filler. The 'no score' disclaimer is purposeful and prevents a common misconception. Slightly more explicit structure could improve it, but it is appropriately sized and front-loaded.

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 tool with no annotations, no output schema, and 0% parameter coverage, the description is too sparse to serve as a complete call contract. It gives a useful mental model but leaves required parameters, response shape, and the meaning of 'transfers' unexplained.

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 0%, and the description does not mention slug or locale at all. It only implies that a degree/major is being looked up, leaving both parameter formats and meaning entirely to inference.

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 names the resource (a degree/major) and what it returns ('the occupations it feeds'), and explicitly disclaims scoring. It distinguishes itself from occupation-centric siblings by focusing on the major, though it uses a noun phrase rather than a clear imperative verb.

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?

It provides a concrete trigger: answer 'should I switch majors' by pointing at where the major leads and what transfers. It does not explicitly name alternatives or exclusions, but the context is clear enough for an agent to recognize when get_major is the intended lookup.

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