academy_tutor

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions 'Pro-only' (implying access restrictions) and context awareness with parameters, but lacks details on rate limits, response format, error handling, or whether this is a read/write operation. For a tool with no annotations, this leaves significant gaps in understanding its behavior.

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 extremely concise with only two sentences, both front-loaded with essential information: the core action and key usage constraints. Every word earns its place, with no redundant or vague phrasing. This efficiency makes it easy for an agent to quickly grasp the tool's purpose.

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 no annotations, no output schema, and 3 parameters with 0% schema coverage, the description is minimally adequate. It covers the basic purpose and some usage context but lacks details on authentication needs, response format, error cases, or parameter examples. For a Pro-only interactive tool, this leaves room for improvement in completeness.

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%, so the description must compensate. It explains that 'level+lessonSlug' provides lesson context to the tutor, adding meaning beyond the schema's type constraints. However, it doesn't clarify the 'message' parameter's purpose or format, or provide examples. With 3 parameters and 0% schema coverage, this partial compensation results in a baseline score.

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's purpose: 'Ask the Academy AI-Tutor a question.' It specifies the resource (Academy AI-Tutor) and action (ask a question), distinguishing it from sibling tools like academy_lesson or academy_quiz. However, it doesn't explicitly differentiate from academy_review or academy_quiz_submit, which might also involve asking questions in different contexts.

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 clear context for usage: 'Pro-only' indicates a prerequisite, and 'The tutor knows the current lesson context if you provide level+lessonSlug' explains when to use specific parameters. It implies this tool is for interactive tutoring rather than content retrieval or assessment. However, it doesn't explicitly state when not to use it or name alternatives among siblings.

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