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

No annotations are provided, so the description carries the full burden. It explains the return structure (courses per semester, specialization points) and the cohort_year default. While it doesn't mention auth or rate limits, for a read-only query tool this is sufficient.

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 two sentences, each adding value. It is front-loaded with the main purpose and includes essential details without redundancy. No wasted words.

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?

Given the tool's complexity, the description thoroughly explains what is returned (courses, codes, credits, mandatory/elective, semesters, specialization groups) and how to use the output. It even notes the cohort_year default. No output schema exists, but the description compensates fully.

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 0%, so the description must compensate. It explains cohort_year in detail, including its default and how to compute it. Program_code is implied by context. Both parameters are effectively described.

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 the tool returns the official study plan for an NTNU program and cohort, detailing courses per semester and specialization choices. It uses specific verbs and nouns and distinguishes itself from siblings by explaining how the output can be fed to other tools.

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 explicit guidance on when to use the tool (as a bridge from program to course codes) and explains the cohort_year parameter's meaning and default. It suggests downstream tools, but does not explicitly state when not to use it.

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