Create an empty course (no modules/lessons). Prefer hivelearn_create_course_outline when you already know the structure — it scaffolds course + modules + lesson placeholders in one call.
| Name | Required | Description | Default |
|---|---|---|---|
| tags | No | ||
| title | Yes | ||
| difficulty | No | ||
| visibility | No | Defaults to all_members | |
| description | No | ||
| thumbnail_url | No | ||
| instructor_name | No | ||
| description_json | No | ||
| description_format | No | ||
| instructor_avatar_url | No |
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 full burden. It discloses a key behavioral trait: the created course will be empty of modules/lessons. While it doesn't mention return values or permissions, for a create operation this scope clarification is valuable and meets the transparency bar.
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 two sentences, front-loaded with the primary action. The second sentence efficiently provides an 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?
For a 10-parameter tool with no annotations and no output schema, the description gives the core purpose and alternative guidance, but leaves parameter details to the schema, which is mostly undocumented. It is minimally viable but lacks richness; additional details on required fields or default behavior would improve completeness.
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 only 10% (only 'visibility' has a description), yet the description adds no parameter-specific meaning. It does not explain fields like 'description_json' or 'difficulty', leaving the agent to infer from names. Since coverage is low, the description needed to compensate but did not.
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 clearly states the tool's function with a specific verb and resource: 'Create an empty course'. It explicitly notes 'no modules/lessons', distinguishing it from creation tools that scaffold structure. It also differentiates from the sibling tool hivelearn_create_course_outline, making the purpose unambiguous.
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?
Provides explicit guidance: 'Prefer hivelearn_create_course_outline when you already know the structure — it scaffolds course + modules + lesson placeholders in one call.' This tells the agent exactly when to use this tool versus an alternative, which is high-quality usage guidance.
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.
