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Live seat and waitlist counts for a course

course_availability

Check real-time seat availability, waitlist counts, CRNs, meeting times, and instructors for any course section to see if you can enroll.

Instructions

How many seats are actually open in each section of a course right now, with waitlist counts, CRNs, meeting times, and instructors. This is the authoritative registration data — use it over search_courses/course_sections when the question is 'can I get in'. Source: Purdue Banner self-service (public, live).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
termNoTerm code ('202710') or name ('Fall 2026'). Defaults to the registerable term.
limitNoMax sections to detail. Default 25.
numberYesCourse number, e.g. '18000' or '180'.
subjectYesSubject abbreviation, e.g. 'CS', 'MA', 'ENGL'.
open_onlyNoOnly sections with seats remaining.
Install Server

TDQS

A4.5/5.0
Behavior4/5

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

With no annotations, the description carries the full burden. It states the source (Purdue Banner self-service, public, live) and the type of data returned. While it lacks explicit mention of read-only status or side effects, the context strongly implies it is a safe read operation. A slightly higher score would require explicit behavioral notes.

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 zero waste. The first sentence front-loads what the tool returns, the second provides usage guidance and source. Every word earns its place.

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

Completeness5/5

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

Despite having no output schema, the description enumerates return fields (seats, waitlist, CRNs, meeting times, instructors) and clarifies it operates per section. Combined with the schema's parameter descriptions, it provides a complete picture for an agent to use the tool correctly.

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

Parameters3/5

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 does not add parameter-level context beyond what the schema already provides. It focuses on output rather than input parameters, which is acceptable given the schema is complete.

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 the tool returns live seat and waitlist counts for course sections, with details like CRNs and instructors. It explicitly distinguishes from sibling tools 'search_courses' and 'course_sections', making the purpose specific and unambiguous.

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

Usage Guidelines5/5

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

The description directly advises using this tool over alternatives when the question is 'can I get in', providing explicit context for when to use and when not. It also labels the data as authoritative registration data, further clarifying its appropriate use case.

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

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