learning_overview

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

Annotations already declare readOnlyHint=true and destructiveHint=false, so the read-only safety profile is covered. The description adds valuable behavioral context beyond annotations: it is a batched single-request read that does not consume a DeepTutor model turn, which informs cost and latency expectations.

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?

A single front-loaded sentence conveys the resource scope, the aggregation behavior, and the key non-obvious trait (no DeepTutor model turn). Every word earns its place; there is no redundant or filler content.

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 an output schema exists, annotations cover safety, and there is only one optional parameter, the description is nearly complete for correct invocation. The only minor gap is not naming granular siblings like get_course_state, list_mastery_paths, or practice_queue as alternatives for deeper detail.

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?

There is only one optional parameter and schema description coverage is 100%, so the schema fully documents 'timezone'. The description adds no parameter-specific details, which is acceptable because the schema carries the burden and the parameter has a clear default.

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 states a specific verb ('Read') and concrete resources ('courses, mastery summaries and due practice'), and the qualifier 'in one request; no DeepTutor model turn' clearly differentiates it from the many granular sibling tools. An agent can immediately recognize this as the aggregate read-only overview endpoint.

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 phrase 'in one request' and 'no DeepTutor model turn' gives clear context: use this when a quick aggregate overview is needed without invoking model-based reasoning. It does not explicitly name alternatives or exclusion conditions, but the intended usage is inferable with high confidence.

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