unw-mcp

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unw_course

Retrieve full details for one course CRN in a term, including meetings, faculty RMP evidence, catalog and prerequisite text, linked lecture/lab sections, enrollment, and availability.

Instructions

Full detail for one CRN: meetings, faculty with RMP evidence, attributes, catalog description, prerequisite/corequisite/restriction text plus the parsed prerequisite codes, linked lecture/lab CRNs, enrollment text and availability.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "term": "<term>",
      "crn": "<crn>"
    },
    "name": "unw_course"
  }
}

Input Schema

Name	Required	Description	Default
`crn`	Yes
`term`	Yes
`refresh`	No

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Nov 12, 2026

TDQS

B3/5.0

Behavior3/5

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

With no annotations, the description carries the full burden; it does imply a read-only detail fetch and its field list signals an expensive aggregate (RMP lookups, catalog text, enrollment). However it never states permissions, latency/caching behavior, or what the 'refresh' flag changes about underlying calls — the one genuinely behavioral detail an agent would want.

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?

A single front-loaded sentence with the purpose first and an itemized payload list after; no filler. It is dense but every clause names a distinct returned field, so little is wasted.

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?

Absent an output schema, enumerating returned fields is the right move and is done well. But for a three-parameter, zero-coverage-schema tool with no annotations, the definition leaves term format, refresh semantics, and error/not-found behavior entirely unexplained.

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

Parameters2/5

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

Schema description coverage is 0% for all three parameters, so the description must compensate and does not. It implies 'crn' by saying 'one CRN' but says nothing about term formatting (e.g. '202510' vs 'Fall 2025'), that crn accepts either string or integer, or what refresh=true actually does.

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?

The description names a specific resource (one CRN) and enumerates exactly what is returned: meetings, faculty with RMP evidence, attributes, catalog text, prerequisite codes, linked lab/lecture CRNs, and enrollment/availability. That is far more specific than a title restatement, but it never contrasts itself with siblings like unw_search or unw_session, which are the obvious entry points for the same data.

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

Usage Guidelines2/5

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

There is no explicit when-to-use statement, no exclusions, and no mention of what an agent should call first to obtain a CRN. The 'one CRN' scoping implies a detail lookup after a search, but that inference is left entirely to the reader.

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