Skip to main content
Glama

hivelearn_get_track_progress

Per-member progress through a learning track: enrolled_at, started_at, completed_at, total_courses, completed counts. Use for cohort/track analytics.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoPage size, default 20
offsetNoRows to skip, default 0
track_idYes

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. Added

TDQS

A3.7/5.0
Behavior2/5

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

No annotations are provided, so the description carries the full burden of behavioral transparency. It discloses what data is returned but does not state whether the operation is read-only, what authentication or permissions are required, or any rate limits or error behavior. For a retrieval tool, this lack of explicit non-mutating guarantee is a gap.

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

Conciseness5/5

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

The description is compact and front-loaded with the tool's purpose, followed by a usage suggestion. Every sentence contributes: the first defines what the tool returns, the second gives context. There is no repetition of schema constraints.

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

Completeness3/5

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

With no output schema and no annotations, the description needs to explain the return shape and safety profile. It lists key output fields but not the full response structure (e.g., whether it's a list object with pagination metadata). It also does not state the read-only nature of the operation. However, the tool is relatively simple, and the schema covers pagination parameters. This is adequate-but-gapped.

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

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema documents limit and offset with descriptions (67% coverage), but track_id has no description. The description implies track_id is the track of interest ('through a learning track') but does not add detail about UUID format or behavior. It also does not explain pagination behavior beyond what schema already says.

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

Purpose5/5

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

The description clearly states this tool returns per-member progress through a learning track, enumerating specific fields (enrolled_at, started_at, completed_at, total_courses, completed counts). This differentiates it from the sibling get_course_progress, which is presumably per-course. The verb ('get') is implied by the tool name, and the resource (track progress) is explicit.

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

Usage Guidelines4/5

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

The description provides a clear use case ('cohort/track analytics'), which helps the agent decide when to invoke it. However, it does not explicitly name alternative tools for course-level progress, such as hivelearn_get_course_progress, nor does it state when not to use it. This is clear context without exclusions.

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

Try in Browser

Glama MCP Gateway

Add one secure layer between your agents and this server.

TDQS

A3.7/5.0
Disambiguation5/5

Every tool targets a distinct resource/action combination, and similar-looking tools are carefully differentiated in descriptions (e.g., get_course_structure vs list_course_modules, update_lesson vs update_lesson_content). There is no meaningful overlap or ambiguity between tools.

Naming Consistency5/5

All tools use a consistent 'hivelearn_<verb>_<noun>' pattern with common verbs (get, list, create, update). The only minor deviation is 'add' vs 'create' (add_track_course vs create_track), but this is semantically appropriate and does not disrupt the overall pattern.

Tool Count2/5

With 57 tools, the server is significantly over the recommended range and exceeds the 25+ threshold for 'too many'. While the broad domain (courses, community, analytics) justifies a large surface, this many tools makes selection overwhelming for agents and suggests a need for consolidation or sub-servers.

Completeness3/5

The tool surface covers create, read, and update for most core entities (courses, lessons, quizzes, tracks, posts, events, resources), plus publishing/verification and analytics. However, there are notable gaps: no delete operations for courses, lessons, modules, quizzes, posts, events, resources, or enrollments, and no way to remove a course from a track. These lifecycle holes are significant but not fatal for common workflows.

Resources