get_course_analytics

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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint, and the description adds valuable behavioral context: the tool returns only aggregates, filter_domains is comma-separated, and the analytics:read OAuth scope is required. No contradiction with annotations exists.

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 tightly packed: a one-sentence summary, then necessary parameter semantics, then the alternative tool and auth requirement. Every sentence adds distinct value with no filler or repetition.

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 two-parameter analytics tool with an output schema, the description covers the core behavior, parameter meaning, scope limitations, alternative tool, and required OAuth scope. Nothing essential is missing for an agent to select and invoke it correctly.

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 carry parameter meaning. It explains course_id identifies the course and is required, and filter_domains narrows figures and is comma-separated. This meaningfully compensates for the bare schema.

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 opens with a specific verb and resource: 'Summarise how one course is performing overall, across its learners.' It clearly distinguishes this aggregate analytics tool from per-learner tools like list_enrollments, and the 'one course' scope is explicit.

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 explicitly states when to use this tool versus alternatives: 'Aggregates only: for per-learner detail use list_enrollments with filter_course_id.' It also notes there is no organization-wide analytics call, so comparing courses requires calling per course. This is strong routing guidance.

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