Edit lesson metadata or move it between modules. To edit ONLY the content body use hivelearn_update_lesson_content (narrower scope, safer).
| Name | Required | Description | Default |
|---|---|---|---|
| id | Yes | ||
| title | No | ||
| module_id | No | ||
| is_preview | No | ||
| sort_order | No | ||
| content_url | No | ||
| description | No | ||
| content_json | No | ||
| content_type | No | ||
| is_published | No | ||
| thumbnail_url | No | ||
| content_format | No | ||
| duration_seconds | 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?
No annotations are provided, so the description carries full responsibility for behavioral disclosure. It only states the action (edit metadata/move between modules) without explaining whether updates are partial or full, whether moving to a module has side effects (e.g., reordering), any permission requirements, or what the response contains. This is a mutation tool with zero annotation coverage; the description adds minimal behavioral context beyond the raw operation.
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?
Two sentences, first states purpose, second gives a targeted alternative. No filler or redundant phrases.
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?
With 13 parameters, no output schema, and no annotations, this description is too terse to be fully operational. It lacks details on return values, update semantics, permission requirements, and the specific metadata fields involved. The pointer to the content tool is useful, but it doesn't compensate for the missing 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 coverage is 0%, and the description must compensate for 13 undocumented parameters, but it only hints at module_id via 'move it between modules' and groups everything else under 'metadata.' It does not explain the meaning of content_type, content_json, is_preview, sort_order, etc., leaving the agent to guess the role of most fields.
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?
Description opens with "Edit lesson metadata or move it between modules," a specific verb+resource statement. It explicitly distinguishes from sibling hivelearn_update_lesson_content by scoping content body edits to that 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 directly tells the agent when to use this tool vs the content-specific sibling: 'To edit ONLY the content body use hivelearn_update_lesson_content (narrower scope, safer).' This provides explicit use case differentiation. It doesn't discuss alternatives like create_lesson or reorder_module_lessons, but the main conflict is resolved.
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.
