This server gives AI agents local, privacy-preserving plain-language tools to score and simplify English text before sending it to humans.
Score text readability: computes Flesch Reading Ease, Flesch-Kincaid Grade Level, sentence/word/syllable counts, and readability labels for English text.
Simplify text: rewrites text into plainer language by splitting long sentences and substituting complex words (e.g., "utilize" → "use", "prior to" → "before").
Compare before/after readability: reports Flesch Reading Ease before and after simplification so you can confirm improvement.
Return Markdown or JSON: both tools support human-readable summaries or structured data output.
Works entirely locally: no API key, network calls, or external services — text never leaves the machine.
Integrates with MCP clients: callable from Claude Code, Claude Desktop, or any MCP-compatible client.
Handles unsupported text safely: non-Latin script is detected and returned unchanged with supported: false instead of producing unreliable results.
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type @ followed by the MCP server name and your instructions, e.g., "@readability-mcp-server Simplify this text: 'Please utilize the enclosed form prior to the deadline.'"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
An MCP server that lets any AI agent check and improve the plain-language quality of its own text — before that text ever reaches a human. Fully local: no API key, no network call, no external service. It's a pure computation tool, so nothing you pass through it ever leaves the machine it's running on.
Unlike the other tools in this series (scamlens, clearread, clauselens), this one isn't a website a person opens — it's infrastructure for the AI agent ecosystem itself. Any MCP-compatible client (Claude Code, Claude Desktop, or any other) can call it directly.
Agents generate a lot of text — replies, documentation, notices, error messages — and mostly have no way to check whether that text is actually easy for the recipient to read before sending it. This server gives any agent that self-check as a first-class tool call, using the same plain-language engine (sentence splitting + a vetted complex-to-plain word dictionary) already shipped and tested in clearread, the standalone web app in this series — here exposed for programmatic use instead of pasted by hand into a browser.
Related MCP server: BeLikeNative Grammar Server
readability_score_textComputes Flesch Reading Ease and Flesch-Kincaid Grade Level for a block of
English text. Returns supported: false with structural counts only for
non-Latin script, since the Flesch formulas assume English syllable
patterns.
readability_simplify_textRewrites text in plainer language — splits overly long sentences at natural
clause boundaries and substitutes common bureaucratic vocabulary for plainer
equivalents — and reports the Flesch Reading Ease score before and after, so
you can confirm the rewrite actually helped. English only, by design: word
substitution is unreliable across languages with richer morphology, so
non-Latin-script text is detected and passed through unchanged rather than
risk broken grammar (see the note in src/services/simplify.ts about why a
few tempting dictionary entries, like "accompanied" → "went with", were left
out after they broke common fixed phrases like "must be accompanied by").
Clone and build:
git clone https://github.com/wedo911/readability-mcp-server.git
cd readability-mcp-server
npm install
npm run buildAdd it to your MCP client's config (e.g. claude_desktop_config.json, or a
project's .mcp.json for Claude Code):
{
"mcpServers": {
"readability": {
"command": "node",
"args": ["/absolute/path/to/readability-mcp-server/dist/index.js"]
}
}
}npm run build
node --test tests/textStats.test.mjs tests/simplify.test.mjsThe MCP Inspector can call tools directly from the command line:
npx @modelcontextprotocol/inspector --cli node dist/index.js \
--method tools/call --tool-name readability_simplify_text \
--tool-arg text="Please utilize the enclosed form prior to the deadline."MIT — see LICENSE.
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).
| Name | Required | Description | Default |
|---|---|---|---|
| text | Yes | The text to score, e.g. a draft reply, an email, or documentation. | |
| response_format | No | Output format: 'markdown' for a human-readable summary or 'json' for structured data. | markdown |
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.
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.
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.
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.
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.
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.
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.
| Name | Required | Description | Default |
|---|---|---|---|
| text | Yes | The English text to simplify, e.g. a draft reply before sending it to a user. | |
| response_format | No | Output format: 'markdown' for a human-readable summary or 'json' for structured data. | markdown |
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.
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.
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.
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.
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.
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.
Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.
v1.0.0readability_score_textreadability_simplify_textThe two tools have clearly distinct purposes: one measures readability metrics, the other rewrites text to simplify it. There is no overlap or ambiguity in choosing between them.
Both tool names follow a consistent pattern: domain prefix 'readability_' plus verb_noun pairs ('score_text', 'simplify_text'). The naming is uniform and predictable.
Two tools is on the thin side for a server, but it is a tightly focused scope: measuring and simplifying readability. It feels slightly minimal but each tool earns its place.
For the stated purpose of assessing and improving plain-language readability, the server covers both key operations: evaluating a text's readability and rewriting it into plainer language. No obvious critical gap exists.
Clean Markdown and AI-readability scoring for any URL. Built for AI agents.
The open, measured register where AI agents evolve written English together.
Scan any URL for AI agent readability — Vercel Spec, llmstxt.org, and agent-protocol manifests.
Prose linter + AI-slop detector: weasel words, passive voice, hedging, and research-cited AI tells
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