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Moltline Educator Toolkit

by com.moltlinestudio

Grade Curve

grade_curve
Read-onlyIdempotent

Summarize how an assessment landed: stats, bands, and outliers. FREE.

Reports mean, median, std dev, quartiles, distribution by percentage band, and flagged low outliers. Typical input {"scores": [88, 92, 75, 61, 84], "max_score": 100} returns {"n": 5, "mean": 80.0, "median": 84.0, "std_dev": ..., "q1": ..., "q3": ..., "distribution": {"90-100%": 1, ...}, "outliers_low": [...], "reading": "..."}.

Use on a list of scores already earned. Not for the readability of the assessment itself (readability_report). Errors: on invalid, missing, or malformed input this tool never raises a protocol error — it returns {"error": ""} (for example {"error": "no scores"}). Every call is read-only and idempotent, so after correcting the input it is always safe to retry.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
scoresYesRaw scores, one per student; at least 1 value, e.g. [88, 92, 75].
max_scoreNoMaximum possible score; must be greater than 0. Default 100.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

TDQS

A4.4/5.0
Behavior4/5

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

Annotations already provide readOnlyHint and idempotentHint. The description adds value by disclosing that the tool never raises protocol errors on invalid input, instead returning an error object with a fix suggestion. This is a behavioral trait beyond what annotations capture.

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 well-structured: purpose sentence, list of stats, example, usage guideline, and error behavior. It is informative but not overly verbose. Minor waste: the 'FREE.' appears extraneous but does not detract significantly.

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 the tool's complexity, the description covers purpose, exact statistics returned, input/output example, usage guidelines, error handling, and distinguishes from sibling. The presence of an output schema reduces the need to detail return values, but the description still provides a concrete sample. Fully complete.

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?

The input schema has 100% description coverage, so the schema already documents the parameters well. The description provides a typical input example and output, but does not add new semantic meaning to the parameters themselves. Baseline 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 starts with 'Summarize how an assessment landed: stats, bands, and outliers,' which clearly states the verb (summarize) and resource (assessment scores). It distinguishes from the sibling 'readability_report' by explicitly noting it is not for the readability of the assessment itself.

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?

The description explicitly states 'Use on a list of scores already earned. Not for the readability of the assessment itself (readability_report).' This provides clear when-to-use and when-not-to-use guidance, and names the alternative sibling tool.

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

All Moltline Educator Toolkit toolsInspect
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TDQS

A4.7/5.0
Disambiguation5/5

All seven tools target distinct actions: product discovery (list_products), free skill retrieval (get_free_skill), paid skill retrieval (get_full_skill), full product details (get_full_product), assessment analysis (grade_curve), lesson timing (lesson_timer), and text readability (readability_report). No overlap or ambiguity.

Naming Consistency5/5

All tools use a consistent verb_noun pattern in snake_case: get_free_skill, get_full_product, get_full_skill, list_products, grade_curve, lesson_timer, readability_report. The naming is predictable and intuitive.

Tool Count5/5

With 7 tools, the server is well-scoped for an educator resource provider. It covers product access (4 tools) and three distinct pedagogical utilities, without unnecessary bloat or deficiency.

Completeness5/5

The tool surface covers the full lifecycle for its domain: product discovery, free and paid skill access, full product retrieval, plus three distinct teaching utilities (assessment summary, lesson timing, readability). No obvious gaps for a read-only resource server.

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