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NCCU-AI-SYSTEM

NCCU Moodle MCP

Get my role in a course

get_course_role

Check your role in a specific Moodle course by providing its course ID. Returns the primary role and all assigned roles, such as student, teacher, or editingteacher.

Instructions

Get the user's role in one course (by Moodle course id, from list_courses): 'student', 'teacher' (often the TA/助教), 'editingteacher' (instructor), 'teachingassistant', 'manager', etc.

Returns {course_id, role, roles}: role is the primary role shortname, roles all of them (a user can hold more than one).

Credentials come from the MCP settings headers, not from you.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
course_idYesMoodle course id (from list_courses).

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4.3/5.0
Behavior4/5

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

With no annotations, the description carries the behavioral disclosure burden. It explains the exact return structure ({course_id, role, roles}), clarifies that a user can hold multiple roles, and explicitly states that credentials come from MCP settings headers rather than from the agent. This is strong transparency for a simple read-only lookup.

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 compact and front-loaded: it states the purpose first, then role values, then return format, then credential handling. Every sentence adds distinct value with no filler or repetition of schema fields.

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?

For a one-parameter tool with no output schema, the description is complete: it identifies the input source, enumerates expected role values, explains the return fields and their meaning, and clarifies authentication. An agent has enough information to call this tool correctly and interpret the result.

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 coverage is 100%, and the schema already documents course_id as a Moodle course id. The description adds useful provenance (from list_courses) and role-value context, but it does not need to compensate for schema gaps. This meets the baseline for a well-covered single parameter.

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 names a specific verb ('Get'), a concrete resource ('the user's role in one course'), and identifies the required input (Moodle course id). It also distinguishes itself from course listing tools by scoping to a single course and returning role data, not course metadata.

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

Usage Guidelines4/5

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

The description clearly indicates that course_id comes from list_courses, giving an agent a concrete prerequisite and source. It does not explicitly state when not to use the tool or name alternatives, but the narrow scope and role-specific output make the usage context clear.

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