Add a course to a track at a given position. sort_order sets the order within the track. Set unlock_after_course_id to a prerequisite course that must be completed first (omit/null for no gate). is_required defaults to true.
| Name | Required | Description | Default |
|---|---|---|---|
| track_id | Yes | ||
| course_id | Yes | ||
| sort_order | Yes | Position within the track | |
| is_required | No | Defaults to true | |
| unlock_after_course_id | No | Prerequisite course id, or null for no gate |
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?
No annotations are provided, so the description carries the transparency burden. It does disclose useful behaviors: sort_order defines ordering, unlock_after_course_id gates completion, and is_required defaults to true. However, it does not mention side effects such as what happens if the course already exists in the track, validation errors, or the return value. This is adequate but not exhaustive.
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 three compact sentences, each carrying useful information. It front-loads the primary purpose and then explains the two most nuanced parameters and a default. There is no redundancy or fluff.
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?
For a straightforward add operation with no output schema, the description covers the essential aspects: what the tool does, how ordering works, how prerequisites work, and the default for is_required. It does not describe the response format or duplicate-handling behavior, but given the tool's simplicity and the absence of an output schema, 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?
Schema description coverage is 60%, and the description compensates by clarifying the semantics of key parameters: 'sort_order sets the order within the track', 'prerequisite course that must be completed first', and 'is_required defaults to true'. Track_id and course_id are self-explanatory names, so no additional detail is needed.
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 opens with a specific verb and resource: 'Add a course to a track at a given position.' It clearly distinguishes this from sibling tools like create_course (which creates a new course) and create_track (which creates a track). The position aspect is a unique differentiator.
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 provides clear context that this tool is for adding an existing course to an existing track, with positioning and prerequisites. It does not explicitly name alternatives or state when not to use it, but the context is unambiguous enough for an agent to select it over related tools.
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.
