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itu-mcp

itu-mcp

by yatuk
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Hybrid

Estimate Relative Grade

estimate_relative_grade
Read-onlyIdempotent

Estimate a likely İTÜ letter grade from raw class scores using relative-grading rules and both official methods; for planning, not official.

Instructions

Estimate a likely letter grade from raw class scores under İTÜ's relative-grading (bağıl değerlendirme) rules, using both official methods: Method 1 (T-score against Table 1, an EXAMPLE class-level table from the regulation — the actual instructor may use different T-score cutoffs) and Method 2 (mean ± standard-deviation multiples, Table 2, a fixed formula with no table lookup). This is an estimate for planning purposes only, not the official grade. class_scores must be every student's raw 0-100 score who counts toward the relative-grading average (VF students are excluded from that average per the regulation and must not be included here).

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": {
      "class_scores": "<class_scores>",
      "my_score": "<my_score>"
    },
    "name": "estimate_relative_grade"
  }
}

Input Schema

NameRequiredDescriptionDefault
my_scoreYes
class_scoresYes

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. Addedv1.2.1

TDQS

A4.5/5.0
Behavior5/5

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

Annotations declare read-only/idempotent safety, and the description adds crucial context beyond that: it explains each method's data dependency (Table 1 example table vs. fixed Table 2 formula), the caveat that instructors may override cutoffs, that output is an estimate, and the VF-exclusion rule for the average. This is genuine behavioral context, not restatement.

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?

Two dense sentences that are front-loaded with the core purpose. The parenthetical about Method 1's example-table caveat is essential, not filler, though the sentence is long and the second sentence packs several clauses that could be split.

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?

For a 2-param estimation tool with a rich caveat set, the description covers the essential guidance (what to pass, VF exclusion, estimate-only nature, method differentiation). An output schema exists so return formatting needn't be explained. The only gap is my_score's expected range/format.

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

Parameters4/5

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

Schema coverage is 0% and param names are opaque, so the description must carry the load. It does well for class_scores (must be every counting student's raw 0-100 score, VF excluded) but says nothing about my_score's constraints or format, leaving that parameter undocumented.

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 precise verb+resource+method: 'estimate a likely letter grade from raw class scores under İTÜ's relative-grading rules' and names both official methods. This is well-differentiated from siblings like obs_get_grade_distribution or obs_calculate_gpa, which are observational/calculation tools rather than an estimation tool.

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?

Clearly scopes usage: 'for planning purposes only, not the official grade' and specifies exactly what class_scores must contain (every counting student, VF excluded). It does not name an alternative tool for official grades, but the audience and purpose are 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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