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wedo911

readability-mcp-server

by wedo911

Score Text Readability

readability_score_text
Read-onlyIdempotent

Score English text for Flesch Reading Ease and Flesch-Kincaid grade level to verify drafts are clear before sending. Compare drafts or confirm plain-language quality.

Instructions

Compute Flesch Reading Ease and Flesch-Kincaid Grade Level for a block of English text.

Use this to check how easy your own draft output (a reply, a document, documentation) will be for a reader to understand before sending it -- useful for agents that want to self-check plain-language quality, or for comparing readability across different drafts.

Args:

  • text (string, 1-20000 chars): The text to score.

  • response_format ('markdown' | 'json'): Output format (default: 'markdown').

Returns: For JSON format: { "supported": boolean, // false if the text contains non-Latin script (Flesch formulas assume English) "fleschReadingEase": number|null, // 0-100, higher = easier; null if unsupported or no words found "fleschKincaidGradeLevel": number|null, // approximate US school grade level "gradeLevelLabel": string|null, // human description, e.g. "standard (around 8th-9th grade)" "sentenceCount": number, "wordCount": number, "syllableCount": number, "avgWordsPerSentence": number, "avgSyllablesPerWord": number }

Examples:

  • Use when: "Is this support-ticket reply easy enough to understand?" -> score the reply text

  • Use when: "Which of these two drafts reads more simply?" -> score both, compare fleschReadingEase

  • Don't use when: the text is not primarily English -- scoring is skipped for non-Latin script and only structural counts are returned

Error Handling:

  • Returns an error if text is empty or exceeds 20000 characters (split long documents into sections first).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
textYesThe text to score, e.g. a draft reply, an email, or documentation.
response_formatNoOutput format: 'markdown' for a human-readable summary or 'json' for structured data.markdown
Behavior5/5

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

The description discloses important behavior beyond the read-only/idempotent annotations: non-Latin text yields supported=false, metric values can be null, only structural counts are returned for unsupported input, and empty or over-long text raises an error. It also documents the JSON return shape in detail, which matters because no output schema is present.

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 front-loaded with a precise purpose and then organized into useful sections: usage, args, returns, examples, and errors. The Args section duplicates schema details, but the rest of the content earns its place because it covers usage scenarios, fallback behavior, and failure conditions.

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?

For a small, low-risk tool with no output schema, the description is unusually complete: it covers supported inputs, output metrics with units and null semantics, error conditions, and common use cases. There is no crucial operational gap for an agent deciding whether to call and how to interpret results.

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 coverage is 100%, so the schema already documents both parameters and their defaults. The description mostly restates the parameter constraints and adds useful-but-not-critical context such as splitting long documents; it does not meaningfully increase parameter-level understanding beyond the schema.

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 opening sentence names a specific operation—compute Flesch Reading Ease and Flesch-Kincaid Grade Level for a block of English text—rather than restating the tool title. The motivating examples ('check a draft reply', 'which draft reads more simply') make its job and distinction from a simplification tool clear.

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

Usage Guidelines4/5

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

The description gives concrete 'use when' scenarios and an explicit 'don't use when' condition for non-English text, which is strong guidance for tool selection. It does not explicitly point to readability_simplify_text as the alternative, but the separation of scoring vs. simplifying is largely implied.

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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