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

Annotations already indicate non-readOnly behavior via readOnlyHint=false, and the description adds valuable context: the request 'counts toward their plan' and requires a connected ScholarNet account token. This discloses side effects and authentication prerequisites beyond what the annotations or schema state.

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?

Three sentences with no wasted words. The core action is front-loaded, followed by concrete use cases and the key prerequisite. Every sentence earns its place and the structure is easy to scan.

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 simple 2-parameter tool with no output schema, the description is complete: it states what result to expect (step-by-step explanation), the account side effects, the prerequisite token, and appropriate use cases. Nothing essential is missing for correct selection and invocation.

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. The description adds beyond the schema by specifying the kinds of questions expected ('explain X', 'help me understand Y', homework/concept questions), which clarifies the intended input to the 'question' parameter beyond just 'The question to ask the tutor.'

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 begins with a specific verb and resource: 'Ask the ScholarNet AI Tutor a question and get a step-by-step explanation.' It also gives concrete use cases ('explain X', 'help me understand Y', homework/concept questions) that make it easy to distinguish from the sibling generation tools like generate_quiz or generate_flashcards.

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 clearly tells the agent when to use the tool via example question types and homework/concept guidance. It does not explicitly state when not to use it or name alternative sibling tools, so it stops short of a 5, but the use-case framing provides strong contextual selection guidance.

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