Moltline Educator Toolkit

Readability Report

readability_report

Read-onlyIdempotent

Analyze teaching text with Flesch-Kincaid readability and leveling advice. FREE.

Typical input {"text": "", "target_grade": 6} returns {"flesch_kincaid_grade": 8.2, "reading_ease": 62.1, "sentences": N, "words": N, "avg_words_per_sentence": N, "long_sentences_over_25w": N, "polysyllabic_words_sample": [...], "target_grade": 6, "gap": 2.2, "suggestions": ["..."]}.

Use on teaching text where the reading level matters. Not for scores students earned (grade_curve) and not for general prose style, which the creator server's analyze_writing covers. Errors: on invalid, missing, or malformed input this tool never raises a protocol error — it returns {"error": ""} (for example {"error": "empty text"}). Every call is read-only and idempotent, so after correcting the input it is always safe to retry.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`text`	Yes	The passage to analyze — at least one full sentence of plain text; must be non-empty.
`target_grade`	No	Target grade level 1-12; when set, the report adds a gap assessment against it. 0 (default) skips it.

Output Schema

TableJSON Schema

Name	Required	Description	Default
No arguments

TDQS

A4.8/5.0

Behavior5/5

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

Beyond annotations (readOnlyHint, idempotentHint, destructiveHint), the description adds error behavior: never raises protocol error, returns error object with fix info. It also confirms idempotency and retry safety. No contradiction with annotations.

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 somewhat long but well-structured: purpose first, then example, usage guidance, error handling, idempotency. Every sentence adds value, though it could be slightly more concise.

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 (2 parameters, output schema available, annotations present), the description covers purpose, usage, error behavior, idempotency, and business context (FREE). It is fully adequate for an AI agent to select and invoke correctly.

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 value with an example input/output showing how target_grade=6 affects the response, clarifying the gap assessment. This exceeds mere schema repetition.

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 analyzes teaching text with Flesch-Kincaid readability and leveling advice. It distinguishes from siblings by explicitly stating it is not for scores students earned (grade_curve) and not for general prose style (covered by analyze_writing).

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 provides explicit when-to-use ('Use on teaching text where the reading level matters') and when-not-to-use ('Not for scores students earned (grade_curve) and not for general prose style'), naming specific alternatives.

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

A4.6/5.0

Disambiguation5/5

All seven tools have clearly distinct purposes. The three product/skill retrieval tools (get_free_skill, get_full_skill, get_full_product) are precisely differentiated by what they return. The remaining four tools (list_products, grade_curve, lesson_timer, readability_report) each target a unique function with no overlap.

Naming Consistency3/5

Tool names use a mix of verb_noun (get_free_skill, get_full_skill, list_products) and noun_noun (grade_curve, lesson_timer, readability_report) patterns. While each name is readable and the convention is not chaotic, the lack of a uniform pattern reduces consistency.

Tool Count5/5

Seven tools is a well-scoped count for an educator toolkit. Each tool addresses a distinct need: product discovery, skill retrieval, assessment analysis, lesson planning, and readability checking. No tool is superfluous, and the number feels complete without being overwhelming.

Completeness4/5

The tool surface covers the core domain: product listing, skill retrieval (free and paid), assessment statistics, lesson timing, and readability analysis. Minor gaps exist, such as the absence of a search or comparison feature across products, but the essential workflows are supported without dead ends.

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