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Glama

plms_compare_syllabus

Read-onlyIdempotent

Compare syllabus rules against LMS deadlines and attendance evidence to verify compliance. Optional observations are hypothetical, not authoritative.

Instructions

Compare exact syllabus rules with LMS deadlines and attendance evidence. Optional observations are hypothetical input, never authoritative records.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
course_idYes
observationsNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A3.6/5.0
Behavior4/5

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

Annotations already declare readOnly, idempotent, non-destructive and openWorld, so safety is covered. The description adds genuinely new behavioral context: that caller-supplied observations are hypothetical and never authoritative records, which tells the agent how input is weighted. It stops short of describing the comparison output, though an output schema exists to carry that.

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?

Two tight sentences, front-loaded with the comparison action, followed by the one caveat that matters about input trust. No filler, nothing repeated from the schema or annotations.

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?

An output schema exists, so return values need not be described, and the safety profile is covered by annotations. What remains thin is the semantics of the observations parameter and the absence of any pointer to the single-source tools an agent might otherwise pick.

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 0%. course_id is self-explanatory, but observations is an opaque array of free-form objects, and the description only gives its epistemic status (hypothetical, non-authoritative) without shape, contents, or expected keys. That partially compensates for the missing schema text but leaves real ambiguity.

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?

States a specific verb (compare) and two concrete resources (syllabus rules, LMS deadlines and attendance evidence), which clearly separates it from single-source siblings like plms_get_syllabus and plms_get_attendance. It doesn't explicitly name those siblings, so routing is inferred rather than stated.

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?

The description implies the scenario (comparing syllabus rules against actual LMS data) but never says when to use this tool rather than plms_get_syllabus, plms_upcoming_deadlines, or plms_get_attendance. No prerequisites, no exclusions, no alternatives named.

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