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wedo911

readability-mcp-server

by wedo911

Simplify Text

readability_simplify_text
Read-onlyIdempotent

Simplify English text into plain language by splitting long sentences and replacing formal vocabulary. Confirm readability improvement with Flesch Reading Ease scores before and after.

Instructions

Rewrite English text in plainer language: breaks overly long sentences at natural clause boundaries and swaps common bureaucratic vocabulary ("utilize" -> "use", "prior to" -> "before") for plainer equivalents. Also reports the Flesch Reading Ease score before and after, so you can confirm the rewrite actually helped.

Use this before sending a reply, document, or notice to a user who may benefit from plainer language -- including as a self-check step for an agent's own drafted output.

Args:

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

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

Returns: For JSON format: { "supported": boolean, // false if text contains non-Latin script (English-only feature); simplifiedText equals the input unchanged in that case "simplifiedText": string, "changeCount": number, // total words/phrases substituted "changes": [ { "start": number, "end": number, "original": string, "simplified": string } ], // offsets index into simplifiedText "truncated": boolean, // true if changeCount exceeds 50 (only the first 50 are listed) "fleschReadingEaseBefore": number|null, "fleschReadingEaseAfter": number|null }

Examples:

  • Use when: "Make this notice easier to read" -> pass the notice text

  • Use when: checking whether your own drafted reply is needlessly complex before sending it

  • Don't use when: the text is not primarily English -- word substitution is skipped for non-Latin script (Arabic morphology in particular makes naive word-swapping unreliable) and the text is returned unchanged, with supported=false

Error Handling:

  • Returns an error if text is empty or exceeds 20000 characters.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
textYesThe English text to simplify, e.g. a draft reply before sending it to a user.
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?

Beyond the annotations declaring it read-only and idempotent, the description discloses important behaviors: non-Latin scripts return supported=false with unchanged text, only the first 50 changes are listed when changeCount exceeds 50, and Flesch scores are reported before and after. It also explains error conditions for empty or overly long input.

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 well-structured and front-loaded: the core behavior appears in the first sentence, followed by usage guidance, argument details, return structure, and error handling. Each section serves a clear purpose and the length is justified by the richness of the behavior being described.

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?

With no output schema present, the description compensates by listing the full JSON return shape, including edge-case fields like truncated and supported. It also covers input constraints, non-English behavior, and concrete usage examples, leaving an agent well-equipped to format inputs and interpret outputs 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?

The schema already covers both parameters 100%, but the description adds value by documenting the JSON response structure, field semantics, and the default response_format. It also gives context for the text parameter through examples like passing a draft reply.

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 action and resource: rewriting English text in plainer language, breaking long sentences, and substituting bureaucratic vocabulary. It also mentions the Flesch Reading Ease output, which helps distinguish it from the sibling scoring tool by emphasizing that the core job is simplification, not mere scoring.

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 explicit use cases, such as simplifying a response, document, or notice, and also works as a self-check for the agent's own drafted output. It gives a clear when-not-to-use case for non-Latin script, though it does not explicitly name the sibling readability_score_text as the alternative when only a score is needed.

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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