HiveLearn

hivelearn_create_event

Create a calendar event. Dates are ISO 8601 strings in UTC. For virtual events set meeting_url; for in-person set location. event_type controls which field the UI shows.

Input Schema

TableJSON Schema

Name	Required	Description
`tags`	No
`title`	Yes
`end_date`	Yes	ISO 8601 timestamp
`location`	No
`timezone`	No	IANA tz, e.g. "America/New_York"
`event_type`	Yes
`start_date`	Yes	ISO 8601 timestamp
`description`	No
`meeting_url`	No
`max_attendees`	No
`cover_image_url`	No
`description_json`	No
`description_format`	No

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

First observedApr 22, 2026

TDQS

A4.3/5.0

Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries the burden and does disclose input format (ISO 8601, UTC) and the relationship between event_type, meeting_url, and location. However, it does not mention return values, side effects, or permission requirements.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is two sentences with no extraneous content. It front-loads the core purpose and provides essential details efficiently.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a 13-parameter tool with no output schema, this description is somewhat minimal. It covers the critical date and event_type logic, but ignores many optional fields and does not describe the response shape, leaving gaps for the agent.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The description adds semantic value by specifying the UTC format for dates and explaining how event_type determines whether meeting_url or location is relevant. This compensates for the low schema coverage, though it leaves many optional parameters unexplained.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states 'Create a calendar event', using a specific verb and resource, and distinguishes it from other create_* siblings by specifying the domain. The additional detail about event_type further clarifies the tool's purpose.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies usage for creating calendar events, but does not explicitly discuss when to use this tool over update_event or list_events. It does provide clear context for creation, but lacks exclusions or alternative recommendations.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

All HiveLearn tools Inspect

Try in Browser

RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

TDQS

A3.7/5.0

Disambiguation5/5

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.

Naming Consistency5/5

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.

Tool Count2/5

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.

Completeness3/5

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.

Resources

Need Help?