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get_course_outline

Retrieve a course syllabus with modules, lessons, and labs, showing completion status and the next suggested lesson to study.

Instructions

Retrieve course modules, lessons, and labs with completion status.

Provides a detailed syllabus breakdown showing which lectures and hands-on labs have been completed, and indicates the next suggested lesson to help the learner identify exactly what to study next.

Args: course_name: Title, slug, or ID of the course (e.g. 'CKA', 'Docker').

Returns: CourseOutlineResponse with modules, lessons, and next suggested item.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
course_nameYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
errorNoError message if the operation or upstream request failed.
modulesNoList of modules with lesson breakdowns.
warningNoDiagnostic warning or configuration guidance message.
course_idYesIdentifier of the course.
course_titleYesTitle of the course.
total_lessonsYesTotal number of lessons across all modules.
total_modulesYesTotal number of modules.
completed_lessonsYesNumber of completed lessons.
next_suggested_lessonNoThe immediate next uncompleted lesson to guide the learner's study plan.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A3.6/5.0
Behavior3/5

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

With no annotations, the description carries the full burden. It implies a read-only retrieval and discloses that completion status and a next suggested lesson are included, which is useful context, but says nothing about authentication requirements, whether enrollment is required, caching, or error behavior for an unknown course.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Front-loaded with the core action in the first sentence, followed by a clarifying sentence and tidy Args/Returns blocks. It is slightly redundant in restating the return type already covered by the output schema, but overall tight.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a single-parameter read tool with an output schema present, the description covers the required input semantics, the returned artifacts, and the study-planning use case. The main gap is the absence of any differentiation from the closely related sibling tools.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

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, and it does: course_name is documented as accepting a title, slug, or ID with concrete examples ('CKA', 'Docker'). That is genuine semantic value the bare 'string' schema does not provide.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb and resource ('Retrieve course modules, lessons, and labs with completion status') and names the concrete artifacts returned, so the agent knows this is a syllabus/structure tool. It does not, however, explicitly distinguish itself from the very similar sibling get_course_progress.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Usage is only implied through the stated motivation ('help the learner identify exactly what to study next'), which does suggest a study-planning context. There is no explicit when-to-use, no exclusion, and no routing to the sibling get_course_progress, which overlaps heavily in purpose.

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