calc.engineer

test grade

test_grade

Calculates a test or exam grade from the number of correct answers and total questions. Returns the percentage score, letter grade, points missed, and GPA points. Supports US grading (A/B/C/D/F with 4.0 GPA scale), UK grading (First/2:1/2:2/Third/Fail), and percentage-only mode. US thresholds: A>=90, B>=80, C>=70, D>=60, F<60. UK thresholds: First>=70, 2:1>=60, 2:2>=50, Third>=40, Fail<40. Useful for students checking scores and teachers computing class statistics.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`total`	Yes	Total number of questions on the test. Must be a positive integer.
`correct`	Yes	Number of questions answered correctly. Must be a non-negative integer.
`grading_scale`	No	Grading scale to use. 'us' for A-F letter grades, 'uk' for First/2:1/2:2/Third/Fail, 'percentage_only' for just the percentage.	us

Output Schema

TableJSON Schema

Name	Required	Description
`gpa_points`	Yes	GPA points on a 4.0 scale (US only). A=4.0, B=3.0, C=2.0, D=1.0, F=0. Null for UK and percentage_only.
`percentage`	Yes	Score as a percentage (0-100).
`letter_grade`	Yes	Letter grade based on the selected grading scale. Null if percentage_only.
`points_missed`	Yes	Number of questions answered incorrectly (total - correct).

TDQS

A4.2/5.0

Behavior4/5

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

With no annotations, the description carries full burden. It discloses the return values (percentage, letter grade, points missed, GPA) and grading thresholds. It could mention error handling or validations like correct ≤ total, but overall covers key behaviors.

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 description is a well-structured paragraph, front-loaded with the purpose. It includes relevant details (return formats, scales, thresholds) without excessive fluff. A minor cut for slightly verbose threshold listings.

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?

Given no annotations and a complex output schema, the description thoroughly covers purpose, parameters, behavior, and return values. It also specifies use cases (students, teachers). No significant gaps.

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 100%, so baseline is 3. The description adds significant value by explaining the grading scales and thresholds, providing context beyond the schema's enum description for grading_scale.

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 calculates a test or exam grade from correct answers and total questions. It distinguishes itself from sibling tools like percentage_calc by offering letter grades and multiple grading scales.

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

Usage Guidelines3/5

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

The description implies use for students and teachers but does not explicitly state when to use this tool versus alternatives like percentage_calc. It lacks guidance on when not to use it or mention of alternative tools.

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

All calc.engineer tools Inspect

Try in Browser

RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

TDQS

A3.9/5.0

Disambiguation4/5

Despite 89 tools, each has a clearly distinct purpose with detailed descriptions that often reference related tools. Overlap exists (e.g., multiple LoRa/RF tools), but the descriptions are sufficient to distinguish them. Some confusion possible among similar-sounding tools like attenuator_pi and attenuator_tee, but the descriptions explicitly compare them.

Naming Consistency4/5

Consistent underscore-separated lowercase naming. Most tools follow a verb_noun pattern (e.g., capacitor_charge, wire_gauge) or noun_noun (power_cost). Minor inconsistencies such as 'bmi_calculator' vs 'solar_sizing' but overall predictable.

Tool Count2/5

89 tools is far too many for a single MCP server. This scope is more appropriate for multiple specialized servers. The sheer number will slow agent selection and increase cognitive load, reducing coherence.

Completeness3/5

Covers many domains (RF, solar, PCB, networking, math, etc.) but lacks depth in some areas (e.g., no three-phase power, no airflow calculations). Some domains have comprehensive coverage (LoRa/Meshtastic), but others feel incomplete for the tool count.

Resources

Need Help?