List community events. Use upcoming=true for future events only (recommended for calendar UI). Returns id, title, start/end dates, event_type, location/meeting_url, attendee counts.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Page size, default 20 | |
| offset | No | Rows to skip, default 0 | |
| upcoming | No | If true, only events with start_date in the future |
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, the description carries the burden of behavioral disclosure. It specifies the return fields and implies that `upcoming=false` (or default) may return all events including past ones, which is useful. However, it does not disclose default sorting, pagination behavior beyond schema, or whether location/meeting_url are conditionally present. This is moderate transparency.
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 purpose, and every sentence adds value. It avoids fluff and includes the essential return fields in a compact, readable format.
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 simple list tool, the description covers the essential output fields, which is crucial given no output schema. It also gives a practical usage tip for the common calendar UI scenario. However, it omits details like default event range (past+future) and ordering, which are relevant for a list operation. These gaps prevent a 5.
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 100%, so baseline is 3. The description adds a recommendation for the `upcoming` parameter ('recommended for calendar UI') that provides context beyond the schema's dry 'If true, only events with start_date in the future.' It does not add new semantics for `limit` or `offset`, so overall it only slightly enhances parameter understanding.
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 'List community events,' which is a specific verb+resource that clearly states the tool's function. It also enumerates the returned fields (id, title, dates, event_type, location/meeting_url, attendee counts), distinguishing it from single-event retrieval like hivelearn_get_event.
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 contextual guidance for the `upcoming` parameter ('Use upcoming=true for future events only (recommended for calendar UI)'), which helps the agent choose parameter values. However, it does not explicitly mention alternatives such as hivelearn_get_event for retrieving a single event, so it falls short of a full when-to-use versus alternatives guideline.
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.
