Search NTNU's study-program catalog (~400 programs) by free text (name, code, keywords), study level (e.g. 'bachelor', 'master', 'ph.d'), and/or city (Trondheim, Gjøvik, Ålesund). Returns program codes like 'MTDT' — the input for get_study_plan. Call this first when you only know a program's name.
| Name | Required | Description | Default |
|---|---|---|---|
| city | No | ||
| level | No | ||
| query | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description covers search capabilities and output codes but omits details like result limits, pagination, or error handling. Adequate but not comprehensive.
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 front-loading purpose and usage, no fluff. Efficient and well-structured.
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 3 optional parameters, no output schema, and no annotations, the description covers main use and parameters adequately. Could mention result count or pagination, but generally complete.
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%, but description explains query as free text, level with examples (bachelor, master, ph.d), and city with examples (Trondheim, Gjøvik, Ålesund), adding meaning beyond bare types.
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 searches NTNU's study-program catalog by free text, study level, and city, distinguishing it from siblings like search_courses and get_study_plan.
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 advises to call this first when only a program name is known and notes that returned codes feed into get_study_plan, giving clear context. It lacks explicit when-not-to-use guidance but is still good.
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.
Each tool targets a distinct aspect of NTNU course data: scheduling, exams, grades, comparisons, search, etc. Overlaps like get_course_schedule and get_weekly_timetable are clearly differentiated by granularity, with descriptions guiding usage.
All tools follow a verb_noun pattern in snake_case, using simple verbs like 'get', 'search', 'check', and 'compare'. The naming is uniform and predictable across all 12 tools.
12 tools is well-scoped for an informational server covering courses, schedules, exams, grades, study plans, and search. Each tool serves a clear purpose without unnecessary redundancy.
The tool set covers core CRUD-like operations for course data, including search, info retrieval, scheduling, exams, grades, and study plans. Minor gaps exist, such as no dedicated tool for study program details or room information, but overall the surface is comprehensive for the domain.