Edit course metadata. Use hivelearn_publish_course instead of setting is_published=true directly — publish runs validation.
| Name | Required | Description | Default |
|---|---|---|---|
| id | Yes | ||
| tags | No | ||
| title | No | ||
| is_pinned | No | ||
| difficulty | No | ||
| visibility | No | ||
| description | No | ||
| is_featured | No | ||
| is_published | 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 of disclosing behavioral traits. It does add a useful warning that setting is_published=true bypasses validation, which is important behavioral context. However, it does not describe other side effects, permissions required, whether edits are reversible, or what the response contains. For a mutation tool, this is only partially transparent.
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 extremely concise, consisting of two sentences. It front-loads the core purpose ('Edit course metadata') and then provides a critical usage warning. Every word earns its place, with no filler or redundancy.
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 tool's complexity (14 parameters, no output schema, no annotations), the description is insufficient. It explains the publishing caveat but leaves out essential operational details such as which fields can be updated, how partial updates work, return values, or required permissions. A minimal description for a 14-parameter mutation tool should provide more context.
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 0%, meaning none of the 14 parameters are explained in the schema. The description does not compensate; it only mentions is_published in the context of a usage warning, not to explain the parameter's meaning. It adds no semantic value to any parameter, making this a significant gap.
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: 'Edit course metadata.' The verb 'edit' and resource 'course metadata' are specific and unambiguous. It also distinguishes itself from the sibling hivelearn_publish_course by explicitly warning against using this tool for publishing.
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 explicitly provides a when-not-to-use guideline: 'Use hivelearn_publish_course instead of setting is_published=true directly — publish runs validation.' This names the alternative tool and the specific condition under which to avoid this tool. It clearly conveys that this tool is for metadata edits, not publication actions.
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.
