Essay Stats MCP

Readability scores

readability_scores

Read-only

Calculate Flesch-Kincaid, Gunning Fog, and SMOG scores to measure text readability and identify how hard a passage is to read.

Instructions

Score how hard a text is to read with three standard formulas: Flesch Kincaid grade level (plus the Flesch reading ease score), Gunning Fog index and SMOG index. Each grade is roughly a US school grade, so 12 is the end of high school and 16 is a college degree. Input: text. SMOG is built for samples of 30 sentences or more, so on shorter text the tool still returns a number but marks it as less reliable. Example: "Give me the Flesch Kincaid, Gunning Fog and SMOG scores for this paper.

Input Schema

Name	Required	Description	Default
`text`	Yes

Output Schema

Name	Required	Description	Default
`smog`	Yes
`words`	Yes
`sentences`	Yes
`syllables`	Yes
`gunning_fog`	Yes
`complex_words`	Yes
`smog_reliable`	Yes
`reading_ease_label`	Yes
`flesch_reading_ease`	Yes
`flesch_kincaid_grade`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.1Oct 31, 2026

TDQS

A4.1/5.0

Behavior4/5

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

Annotations already declare readOnlyHint=true and openWorldHint=false, so safety is covered. The description adds useful behavioral context: the three formulas, how grades map to US school levels, and that SMOG results are marked less reliable on short texts. It does not fully explain the output format, but an output schema exists.

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 front-loaded with the core purpose and is appropriately sized. Formula names, grade interpretation, input note, reliability caveat, and example each serve a clear function without repetition or padding.

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 simple one-parameter input schema, existing output schema, and annotations covering safety, the description provides enough context to call the tool correctly. It explains what is measured, how to interpret grades, and the main reliability caveat for SMOG.

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 0%, so the description must compensate. It identifies the single input as 'text' and adds a relevant constraint: SMOG is intended for samples of 30 sentences or more, with shorter text marked less reliable. This is meaningful but still leaves format and language expectations unstated.

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 states a specific verb and resource: score text readability. It names the exact formulas (Flesch Kincaid, Flesch reading ease, Gunning Fog, SMOG), which clearly distinguishes it from siblings like count_text, style_flags, and word_frequency.

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?

Usage is implied through the purpose and an example prompt, but there is no explicit guidance on when to choose this tool over siblings or when not to use it. The example clarifies the intended request pattern but does not provide exclusions or 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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