Historical grade distribution for an NTNU course (per year and semester, with counts and percentages; grades A-F, or pass/fail codes). Accepts bare codes like 'TDT4100'. Omit years to get all recorded years. Small counts may be privacy-masked. Data comes from Norway's official DBH statistics.
| Name | Required | Description | Default |
|---|---|---|---|
| years | No | ||
| course_code | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full disclosure burden. It reveals behavioral traits: small counts may be privacy-masked, the data source (DBH statistics), and the default behavior of returning all years if omitted. This is comprehensive 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 concise, with three sentences that front-load the main purpose and add important details. Every sentence earns its place without redundancy.
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 no output schema and no annotations, the description covers core functionality, data source, and privacy masking. It lacks explicit details about the return format (e.g., JSON structure), but the tool is simple enough that an agent can infer the output from the description. Minor completeness gap.
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?
Despite 0% schema coverage, the description adds meaning by explaining the 'course_code' format (bare codes like 'TDT4100') and the 'years' parameter behavior (omit for all years). It could be improved by explicitly stating that years are integer values (e.g., 2023), but overall it compensates for the schema gap.
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 retrieves historical grade distributions for an NTNU course, specifying data granularity (per year/semester), grade categories, and acceptance of bare course codes. It differentiates from sibling tools like get_course_info and get_exam_info by focusing specifically on grade distribution.
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 usage guidance by explaining how to format course codes and the effect of omitting the 'years' parameter. While it does not explicitly state when to use this tool versus alternatives, the context makes it clear; the lack of explicit exclusions is acceptable given the clear purpose.
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.