hivelearn_get_quiz
Fetch one quiz with metadata (passing_score, time_limit, max_attempts). Questions are NOT included — use hivelearn_list_quiz_questions.
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
| id | Yes |
Fetch one quiz with metadata (passing_score, time_limit, max_attempts). Questions are NOT included — use hivelearn_list_quiz_questions.
| Name | Required | Description | Default |
|---|---|---|---|
| id | Yes |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the burden of behavioral disclosure. It adds important context about what is not included (questions) and what metadata fields to expect, but it does not disclose error behavior, authentication requirements, or return format beyond the listed fields.
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 a single, front-loaded sentence that immediately states the primary action and resource, then adds one clarifying exclusion and a concrete alternative. Every word earns its place, with no redundant information.
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 the low complexity (one parameter, no output schema), the description covers the key aspects: what is returned, what is not returned, and where to go for the missing part. It falls short only in omitting possible error/not-found behavior, but for a simple get-by-id tool this is a minor gap.
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?
The schema has one 'id' parameter with no description (0% coverage). The tool description does not explicitly explain that 'id' refers to the quiz ID, though the tool name and 'Fetch one quiz' make it inferable. This is minimally sufficient but does not fully compensate for the missing schema description.
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 uses a specific verb 'Fetch' with a clear resource 'one quiz' and enumerates the included metadata (passing_score, time_limit, max_attempts). It also explicitly differentiates from sibling tools by stating questions are not included and naming the alternative tool.
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 clearly indicates when to use the tool (fetching a single quiz's metadata) and provides an explicit when-not-to-use scenario by stating questions are excluded and pointing to hivelearn_list_quiz_questions. It does not mention using hivelearn_list_quizzes for fetching multiple quizzes, but the 'one quiz' phrasing implies the distinction.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
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.
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.
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.
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.