get_student_analytics

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

Annotations declare readOnlyHint=true and openWorldHint=true, establishing safety and volatility. The description adds value by detailing the returned data (page views, participations, submission timeline). It does not contradict annotations and provides useful behavioral context beyond them.

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 sentences with no wasted words. The first sentence introduces the purpose, and the second specifies the return fields. This front-loaded structure aids quick understanding.

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?

No output schema is provided, but the description adequately explains the return format (page views, participations, submission timeline). For a straightforward read operation with well-documented parameters, this is sufficient. It could mention date range or pagination, but not necessary given simplicity.

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?

Both parameters (course_id, student_id) have descriptions in the input schema, achieving 100% coverage. The description does not add further meaning beyond what the schema already provides, so a baseline score of 3 is appropriate.

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 action ('Get'), the specific resource ('per-student activity analytics'), and what it returns ('page views, participations, and submission timeline'). It effectively distinguishes from sibling tools like 'get_course_analytics' which likely provides aggregate course-level data.

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 implies usage for a specific student within a course, contrasting with course-level analytics tools. However, it does not explicitly state when not to use it or mention alternatives, leaving some ambiguity for an AI agent.

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