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Glama

get_course_progress

Retrieve completion percentage, completed vs total labs, and last activity for all enrolled KodeKloud courses or a specific course by name/ID.

Instructions

Get completion percentage, completed vs total labs, and last activity.

Fetches overall progress metrics across all enrolled courses or filters specifically for a given course name or ID (e.g. 'CKA' or 'Kubernetes').

Args: course_name: Optional course title or slug to filter progress for. If omitted, returns progress for all enrolled courses.

Returns: CourseProgressResponse with completion percentages and lab counts.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
course_nameNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
errorNoError message if the operation or upstream request failed.
coursesNoList of course progress records matching the query.
warningNoDiagnostic warning or configuration guidance message.
raw_dataNoRaw upstream payload if schema drift was detected.
total_courses_enrolledYesTotal number of enrolled courses.
average_progress_percentYesOverall completion percentage across all courses.

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?

No annotations are provided, so the description carries the full burden. The 'Get' framing and the read-only nature of progress metrics imply a safe read, and the description discloses the returned fields, but it says nothing about auth requirements, pagination, or whether missing/unenrolled courses error out.

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 metric summary, then a compact Args/Returns block. Efficient overall, though the 'Returns' section partly restates what the output schema already covers.

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?

With an output schema present, the return-value explanation is largely redundant but harmless. The description covers the single optional parameter and the scope of the query well enough to call the tool correctly; only edge-case behavior is missing.

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: it explains that course_name is optional, accepts a title or slug, gives concrete examples ('CKA', 'Kubernetes'), and states the default behavior when omitted. It could still clarify matching semantics (partial vs exact).

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 ('Get completion percentage, completed vs total labs, and last activity') and names the concrete metrics returned. It is clearly a progress-read tool, but it never explicitly distinguishes itself from close siblings like get_learning_summary or list_enrolled_courses.

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 implied: omit course_name for all courses, pass a name/ID to narrow scope. There is no explicit when-to-use versus siblings such as get_learning_summary, and no exclusions or prerequisites are stated.

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