JobDoneBot

Grade Calculator

grade-calculator

Convert 3-aspect evaluations to ABC and 5-level grades.

Input Schema

TableJSON Schema

Name	Required	Description
`aThreshold`	No	Threshold for A grade (%)
`bThreshold`	No	Threshold for B grade (%)
`attitudeMax`	No	Attitude max score
`thinkingMax`	No	Thinking & Expression max score
`knowledgeMax`	No	Knowledge & Skills max score
`attitudeScore`	Yes	Attitude score (主体的態度の得点)
`thinkingScore`	Yes	Thinking & Expression score (思考・判断・表現の得点)
`knowledgeScore`	Yes	Knowledge & Skills score (知識・技能の得点)

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

Changed10 schema fields changedFeb 16, 2026

addedInput schema / $schema

Added value: +"http://json-schema.org/draft-07/schema#"

addedInput schema / properties / aThreshold

Added value: +{
+  "default": 80,
+  "description": "Threshold for A grade (%)",
+  "maximum": 100,
+  "minimum": 0,
+  "type": "number"
+}

addedInput schema / properties / attitudeMax

Added value: +{
+  "default": 100,
+  "description": "Attitude max score",
+  "minimum": 1,
+  "type": "number"
+}

addedInput schema / properties / attitudeScore

Added value: +{
+  "description": "Attitude score (主体的態度の得点)",
+  "minimum": 0,
+  "type": "number"
+}

addedInput schema / properties / bThreshold

Added value: +{
+  "default": 50,
+  "description": "Threshold for B grade (%)",
+  "maximum": 100,
+  "minimum": 0,
+  "type": "number"
+}

addedInput schema / properties / knowledgeMax

Added value: +{
+  "default": 100,
+  "description": "Knowledge & Skills max score",
+  "minimum": 1,
+  "type": "number"
+}

addedInput schema / properties / knowledgeScore

Added value: +{
+  "description": "Knowledge & Skills score (知識・技能の得点)",
+  "minimum": 0,
+  "type": "number"
+}

addedInput schema / properties / thinkingMax

Added value: +{
+  "default": 100,
+  "description": "Thinking & Expression max score",
+  "minimum": 1,
+  "type": "number"
+}

addedInput schema / properties / thinkingScore

Added value: +{
+  "description": "Thinking & Expression score (思考・判断・表現の得点)",
+  "minimum": 0,
+  "type": "number"
+}

addedInput schema / required

Added value: +[
+  "knowledgeScore",
+  "thinkingScore",
+  "attitudeScore"
+]

First observedFeb 14, 2026

TDQS

B3.2/5.0

Behavior2/5

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

With no annotations, the description carries the full burden of behavioral disclosure. It indicates a pure conversion but does not mention how out-of-range scores are handled, the significance of threshold parameters, or the structure of the returned result. This is minimal for a calculator tool with eight parameters.

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 a single, clear sentence that efficiently conveys the core functionality without unnecessary words. It is front-loaded and immediately understandable.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's complexity (eight parameters, three required) and no output schema, the description is insufficiently comprehensive. It does not explain the grading algorithm, threshold defaults, or return format, leaving the agent without a clear expected output structure.

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 100%, with all eight parameters receiving descriptions. The tool description adds no extra parameter semantics beyond the schema, so a baseline score of 3 is appropriate.

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 clearly states the tool's function: converting three-aspect evaluations into ABC and five-level grades. This specific verb+resource combination distinguishes it from sibling tools like age-calculator or math-evaluator.

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?

No guidance is provided on when to use this tool or how it compares to alternatives. The description implies usage for grading but lacks explicit use cases, prerequisites, or exclusions.

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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TDQS

B3.1/5.0

Disambiguation2/5

Multiple tools have overlapping purposes, such as bg-remover, bg-remover-pro, pro-matting, and takumi all performing background removal, and upscaler/upscaler-pro being redundant. With 202 tools, an agent may easily select the wrong one despite detailed descriptions.

Naming Consistency4/5

Most tools use a consistent kebab-case format with descriptive names like pdf-compress, image-resizer, and tax-return-calc. Exceptions like 'takumi', 'pro-matting', and '-pro' suffixes (bg-remover-pro, upscaler-pro) are minor deviations relative to the total.

Tool Count1/5

202 tools is an extreme mismatch for an MCP server, far exceeding the typical 3-15 well-scoped range. The sheer volume makes it unwieldy for an agent to efficiently navigate and select the right tool.

Completeness4/5

The tool set provides extensive coverage across many domains, including PDF operations (20+ tools), image editing, financial calculations, e-commerce fee estimation, and YouTube utilities. Minor gaps exist in cross-tool integration, but the breadth is highly comprehensive for the apparent purpose.

Resources

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