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Learning Units

learning_units
Read-only

What courses does this institution publish? Filter by subject abbreviation, course code (spaces and case ignored), or title words; each course carries its published credit string verbatim (ranges stay ranges), edition, and the captured page.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
codeNoOptional course code, e.g. BIO 201
limitNoOptional page size (default 50, at most 100)
queryNoOptional title words
offsetNoOptional: the next_offset from a previous answer
unitidYesThe institution's IPEDS UNITID
subjectNoOptional subject abbreviation, e.g. BIO
unit_typeNoOptional learning-unit type, one of the answer's unit_types, e.g. course

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changed
    • addedInput schema / properties / unit_type
      Added value: +{
+  "description": "Optional learning-unit type, one of the answer's unit_types, e.g. course",
+  "type": "string"
+}
  2. Changed2 schema fields changed
    • addedInput schema / properties / subject / examples
      Added value: +[
+  "RNSG"
+]
    • addedInput schema / properties / unitid / examples
      Added value: +[
+  "225070"
+]
  3. Added

TDQS

A3.6/5.0
Behavior3/5

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

Annotations declare readOnlyHint=true so safety is covered. The description adds notable behavioral detail: the credit string is kept verbatim with ranges preserved, and it discloses page capture. However it omits pagination mechanics beyond what the schema says and no return shape.

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

Conciseness4/5

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

Front-loaded with a question framing the tool's purpose, then a compact clause packing filters and output semantics. Dense but no obvious waste; slightly long single sentence.

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

Completeness4/5

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

For a 7-param read-only list tool with no output schema and 100% schema coverage, the description covers purpose, filters, and key output guarantees (verbatim credits, edition, page). Pagination via offset/next_offset is only referenced in schema, and no return structure is given, but the essentials are present.

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?

Schema coverage is 100% so baseline is 3, but the description adds value beyond the schema: it clarifies 'spaces and case ignored' for code and that the filter matches subject abbreviation, course code, or title words. That extra matching semantics goes beyond the schema descriptions.

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

Purpose4/5

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

Clear verb+resource: it lists published courses for an institution and names the filters available. It distinguishes itself from siblings like get_learning_unit (singular) and search_certifications, though it doesn't explicitly name an alternative.

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

Usage Guidelines3/5

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

The lead question implies usage (querying an institution's course catalog with filters), but there is no explicit when-to-use vs when-not, nary a mention of the singular get_learning_unit alternative. Usage context is implied rather than stated.

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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