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yatuk

itu-mcp

by yatuk
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Hybrid

Calculate GPA

obs_calculate_gpa
Read-onlyIdempotent

Calculate a term GPA estimate from OBS registered courses, compare it with the official term record, and test projected grades for what-if scenarios using İTÜ’s 4.00 scale.

Instructions

Calculate a term GPA estimate from OBS registered courses and compare it with the official term record. For factual average questions use preferred_term_gpa and preferred_term_gpa_source; the legacy gpa field remains the calculated estimate. This is a term average, not cumulative GANO. Supports projected grades for what-if scenarios. Uses İTÜ 4.00-scale letter grade conversion.

Input Schema

NameRequiredDescriptionDefault
semesterNo
include_officialNo
projected_gradesNo

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed5 schema fields changedv1.2.1
    • addedInput schema / properties / include_official
      Added value: +{
+  "default": true,
+  "title": "Include Official",
+  "type": "boolean"
+}
    • 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: -"obs_calculate_gpaDictOutput"New value: +"obs_calculate_gpaOutput"
  2. Addedv0.2.3

TDQS

A4.1/5.0
Behavior4/5

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

Annotations already establish read-only, idempotent, non-destructive, open-world behavior, but the description adds useful context: the İTÜ 4.00-scale conversion, projected-grade what-if support, and the term-vs-cumulative distinction. It does not describe side effects or rate limits, but for a read-only calculation tool that is minor.

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?

The core purpose is front-loaded in the first sentence and each sentence carries content. The middle sentence about preferred_term_gpa/preferred_term_gpa_source is somewhat dense field-selection advice that could arguably live with the output schema, but it is still decision-relevant.

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 re-explained. The description covers the operation's semantics (term average, scale, what-if, official comparison) well, leaving only the undocumented semester parameter as a gap for an agent to resolve.

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%, so the description must carry parameter meaning. It addresses projected_grades ('what-if scenarios') and implies include_official ('compare it with the official term record'), but the semester parameter is never explained or even referenced, and the format of the projected_grades map is left entirely to the schema.

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 ('Calculate') and resource ('term GPA estimate from OBS registered courses') and distinguishes the scope from cumulative GANO explicitly. It also clarifies that the operation compares against the official term record and supports what-if projected grades, which separates it from siblings like calculate_target_gpa and estimate_relative_grade.

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?

Provides clear guidance on which output field to use for factual average questions versus the calculated estimate, and states the scope exclusion (term average, not cumulative). However, it never names or rules out the closely related sibling tools (calculate_target_gpa, estimate_relative_grade), so tool-to-tool selection is left partly to inference.

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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