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itu-mcp

by yatuk
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Hybrid

Get Course Grades

get_course_grades
Read-onlyIdempotent

Retrieve a course's Ninova 'Notlar' page grades as entered by the instructor, not official transcript grades; use the OBS grades tool for official letter/midterm grades.

Instructions

Read the Ninova 'Notlar' page for a course — grades as the instructor entered them in the LMS, not the official transcript record. For the official letter/midterm grade, use obs_get_course_grades instead.

Input Schema

NameRequiredDescriptionDefault
courseYes

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed4 schema fields changedv1.2.1
    • removedOutput schema / additionalProperties
      Removed value: -true
    • addedOutput schema / properties
      Added value: +{
+  "result": {
+    "additionalProperties": true,
+    "title": "Result",
+    "type": "object"
+  }
+}
    • addedOutput schema / required
      Added value: +[
+  "result"
+]
    • changedOutput schema / title
      Previous value: -"get_course_gradesDictOutput"New value: +"get_course_gradesOutput"
  2. First observedv0.2.0

TDQS

A4.3/5.0
Behavior4/5

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

Annotations already declare readOnly, idempotent, and non-destructive, so the safety profile is covered. The description adds valuable behavioral context by identifying the data source (LMS-entered vs official record), though it says nothing about auth requirements, caching, or freshness of the LMS data.

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 clauses, zero waste, front-loaded with the purpose and immediately followed by the disambiguation routing. Every sentence earns its place.

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?

With an output schema present, return values needn't be explained. Purpose, data-source distinction, and the alternative are all present; the only gap is the undocumented 'course' identifier format, which is a minor omission for a single-param tool.

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?

The single 'course' parameter has 0% schema description coverage, and the description only says 'for a course' without specifying the expected format (course code, section ID, name). For a low-coverage schema the description should compensate by clarifying the identifier format, which it does not.

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?

States a specific verb (Read) and resource (the Ninova 'Notlar' page for a course) and clarifies the exact data semantics — instructor-entered LMS grades, not the official transcript. It explicitly names the sibling it is distinct from (obs_get_course_grades), so an agent can separate the two without opening either schema.

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?

Provides both when-to-use (LMS/instructor grades) and when-not-to-use with a named alternative (obs_get_course_grades for official letter/midterm grades). The routing condition is explicit and unambiguous.

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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