List community feed posts, newest first. Use category to filter (e.g. "announcements"). Returns id, title, content, category, author, reply counts, timestamps.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Page size, default 20 | |
| offset | No | Rows to skip, default 0 | |
| category | No | Filter by category slug |
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 full burden. It discloses the ordering behavior ('newest first'), the filter capability, and the return fields (id, title, content, category, author, reply counts, timestamps). This goes beyond the schema and gives the agent a clear picture of what to expect. It does not mention error conditions or authentication, but for a read-only list operation, this is sufficient.
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 main action ('List community feed posts, newest first'), followed by a concise usage hint and output specification. No unnecessary words or repetition of schema details.
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 with three parameters, the description is complete: it states the purpose, ordering, filter option, and return fields. The absence of an output schema is compensated by explicitly listing the returned fields. The limit/offset parameters are adequately described in the schema, so no additional explanation is needed.
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?
The input schema covers all three parameters with descriptions, so baseline is 3. The description adds value by providing an example value for the category parameter ('announcements') and indicates that the output includes the category field. This helps clarify the semantics of the category filter beyond just 'filter by category slug'.
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 lists community feed posts with a specific ordering ('newest first'). It distinguishes itself from sibling tools like hivelearn_get_post (singular) and other list tools by naming the resource as 'community feed posts'. The verb 'List' is specific and the additional details about filtering and return fields add clarity.
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 for when to use the tool: for listing posts. It includes explicit usage guidance for the category parameter with an example ('announcements'). It does not explicitly exclude alternatives (like using hivelearn_get_post for a single post), but the plural 'posts' and the listing nature make the intended use obvious.
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.
