This server analyzes text for writing style issues and AI-generated content patterns. It provides the following tools:
check_text: Analyze text (plain or markdown) for 8 categories of writing problems — weasel words, passive voice, duplicate words, long sentences, nominalizations, hedging, filler adverbs, and AI tells. Returns structured results with issue positions, line/column numbers, and context. Accepts an optional config object to customize detectors and a format option (plain or markdown) to skip code blocks, tables, and headings.
fix_duplicates: Automatically remove adjacent repeated words from text and return the cleaned version.
list_word_lists: Retrieve metadata about all detector word lists, including item counts and sample entries (e.g., 95 weasel words, 98 AI tell words, 100 hedging phrases, etc.).
check_file (local server only): Read a file directly from disk and analyze it for style issues. Automatically discovers and applies .wscrc.json configuration files by searching upward from the file's directory, with optional config override support.
Allows running the Writing Style Checker in GitHub Actions CI to automatically check files for writing issues with configurable options.
This server has a remotely hosted endpoint. Open its connector listing for connection details, authentication requirements, and setup instructions:
A prose linter and AI-slop detector. WSC finds AI tells — words, phrases, and sentence structures overrepresented in AI-generated text, each flag backed by a published corpus study. It also catches classic writing issues: weasel words, passive voice, duplicate words, long sentences, nominalizations, hedging, and filler adverbs. Available as a web editor, HTTP API, MCP server, CLI, and GitHub Action.
Web Editor - Real-time highlighting with inline fix buttons for all 8 detectors
HTTP API - POST text with optional config, retrieve structured JSON responses
MCP Server (Remote) - Connect AI assistants via Streamable HTTP transport
MCP Server (Local) - Stdio-based server via wsc-mcp on npm
CLI - Check files from the command line via wsc-lint
GitHub Action - Run checks in CI with ::warning annotations
Configurable - Customize detectors with .wscrc.json files
Related MCP server: TrueVoice MCP
WSC flags patterns that research on AI-generated text finds overrepresented, and cites a source for every flag. It does not, and cannot, prove authorship. Classifier-based detectors carry a documented false-accusation risk: a Stanford study found that seven of them misflagged 61% of essays written by non-native English speakers. WSC avoids that trap by design — every flag is a specific, explainable edit that improves the text no matter who, or what, wrote it.
Detector | Items | Description |
Weasel Words | 95 words/phrases | Vague terms like "very", "basically", "arguably", "numerous" |
Passive Voice | 260 irregular verbs | Auxiliary verbs + past participles (regular |
Duplicate Words | — | Adjacent repeated words across whitespace, case-insensitive |
Long Sentences | threshold: 30 words | Sentences exceeding a configurable word count |
Nominalizations | 245 word pairs | Nouns replaceable with verbs ("utilization" → "use") |
Hedging | 100 phrases | Phrases that weaken assertions ("I think", "it seems") |
Filler Adverbs | 139 words | Adverbs adding emphasis without substance ("totally", "utterly") |
AI Tells | 98 words (+111 inflected forms) + 83 phrases + 12 structural patterns | Words, phrases, and sentence constructions overrepresented in AI-generated text ( |
Word lists sourced from Matt Might's shell scripts and expanded with additional entries. AI tells draw on published corpus studies: Kobak et al. 2025 (Science Advances), Juzek & Ward 2025 (COLING), Liang et al. 2024 (Stanford), and Reinhart et al. 2025 (PNAS). Wikipedia's editor-maintained Signs of AI writing catalogue and AI-detection vendor reports round out the sources.
Create a .wscrc.json to customize detectors. All tools (API, MCP, CLI) support it.
{
"$schema": "https://wsc.theserverless.dev/schema.json",
"ignore": ["CHANGELOG.md", "docs/vendor/**"],
"detectors": {
"weaselWords": {
"enabled": true,
"add": ["synergy", "leverage"],
"remove": ["very"]
},
"longSentences": { "maxWords": 25 },
"adverbs": { "enabled": false }
}
}Every field is optional. Missing fields use defaults. JSON Schema provides autocompletion in VS Code.
ignore is an array of glob patterns relative to the working directory. The GitHub Action uses the repository root. The CLI adds these patterns to its built-in **/node_modules/** and **/.git/** skips, together with the Action exclude input and the CLI --ignore flag.
POST /api/checkAnalyze text for writing style issues. Accepts optional config object.
curl -X POST https://wsc.theserverless.dev/api/check \
-H "Content-Type: application/json" \
-d '{"text":"The code was written very quickly."}'Response:
{
"summary": {
"total": 2,
"weaselWords": 1,
"passiveVoice": 1,
"duplicateWords": 0,
"longSentences": 0,
"nominalizations": 0,
"hedging": 0,
"adverbs": 0
},
"issues": {
"weaselWords": [{ "word": "very", "index": 21, "line": 1, "column": 22, "context": "..." }],
"passiveVoice": [{ "phrase": "was written", "index": 9, "line": 1, "column": 10, "context": "..." }],
"duplicateWords": [],
"longSentences": [],
"nominalizations": [],
"hedging": [],
"adverbs": []
},
"meta": { "characterCount": 34, "wordCount": 6, "sentenceCount": 1, "processingTimeMs": 2 }
}With config:
curl -X POST https://wsc.theserverless.dev/api/check \
-H "Content-Type: application/json" \
-d '{"text":"The code was written very quickly.", "config":{"detectors":{"weaselWords":{"enabled":false}}}}'GET /api/checkReturns API documentation as JSON.
GET /api/detectorsReturns the list of all 8 detectors with descriptions, configurability, and word counts.
GET /healthRuns a smoke test with known text and returns {"status":"healthy"} or 503.
Limits: Max 100,000 characters per request. CORS enabled for all origins.
The Writing Style Checker is available as an MCP server, letting AI assistants check your writing directly.
Tool | Description |
| Analyze text for all 8 writing style issues. Accepts optional |
| Remove duplicate adjacent words and return cleaned text |
| Return info about all detector word lists |
| (Local only) Read a file from disk and analyze it. Auto-discovers |
Connect any MCP client to the hosted server - no installation required.
{
"mcpServers": {
"writing-style-checker": {
"type": "url",
"url": "https://wsc.theserverless.dev/mcp"
}
}
}Install via npm for local usage. Includes check_file for analyzing files on disk with auto-discovery of .wscrc.json.
npx wsc-mcpClaude Desktop / Claude Code config:
{
"mcpServers": {
"writing-style-checker": {
"command": "npx",
"args": ["wsc-mcp"]
}
}
}See the wsc-mcp npm page for full documentation.
Check files from the command line.
# Check all markdown files
npx wsc-lint check "**/*.md"
# Read from stdin
echo "The code was written very quickly." | npx wsc-lint check --stdin
# JSON output for scripting
npx wsc-lint check "**/*.md" --format json
# GitHub Actions annotations
npx wsc-lint check "**/*.md" --format github
# Create a config file
npx wsc-lint initSee the wsc-lint README for full documentation.
- uses: theserverlessdev/wsc@v1
with:
files: '**/*.md'
max-warnings: 20
exclude: |
CHANGELOG.md
vendor/**Input | Default | Description |
|
| Glob pattern for files to check |
| — | Path to |
| unlimited | Max warnings before failing |
|
| On a pull request, check changed files that match |
| — | Glob patterns to skip. Separate with a comma or a new line. Adds to config |
The web editor runs in your browser - we never send text to any server. The API and MCP endpoints only process text you explicitly send to them.
.
├── src/
│ ├── core/ # Shared detection engine
│ │ ├── detector.ts # 8 detection algorithms
│ │ ├── words.ts # Word/phrase lists (800+ entries)
│ │ ├── config.ts # Config types, merging, validation
│ │ ├── config-node.ts # Node-only: file loading, discovery
│ │ ├── analyzer.ts # Unified analyzeText() entry point
│ │ └── index.ts # Public API exports
│ ├── docs/ # Documentation content (Markdown files)
│ ├── mcp/
│ │ └── handler.ts # MCP JSON-RPC 2.0 handler
│ ├── lib/
│ │ ├── App.svelte # Main editor page component
│ │ ├── stores/theme.ts # Theme store (light/dark/system)
│ │ └── components/ # UI components (StatsBar, ConfigPanel, etc.)
│ ├── routes/
│ │ ├── +layout.svelte # Shared layout (header, nav, footer)
│ │ ├── api/check/+server.ts # HTTP API endpoint
│ │ ├── mcp/+server.ts # MCP endpoint
│ │ ├── health/+server.ts # Health check endpoint
│ │ ├── docs/+page.svelte # Documentation page
│ │ └── words/+page.svelte # Word library browser
│ └── styles/
│ └── main.scss # Global styles (light + dark themes)
├── mcp-server/ # Standalone stdio MCP server (npm: wsc-mcp)
├── cli/ # CLI tool (npm: wsc-lint)
├── action/ # GitHub Action (composite)
├── tests/ # 341 tests across 18 files
├── static/
│ ├── schema.json # JSON Schema for .wscrc.json
│ ├── llms.txt # AI/LLM discovery file
│ └── llms-full.txt # Detailed LLM context
├── wrangler.toml # Cloudflare Workers config
└── svelte.config.js # SvelteKit configurationgit clone https://github.com/theserverlessdev/wsc.git
cd wsc
npm install
npm run devVisit http://localhost:5173. The API is at /api/check, MCP at /mcp, health at /health.
Command | Description |
| Start dev server |
| Build for production |
| Type check with svelte-check |
| Run all 341 tests |
| Coverage report |
Deployed as a Cloudflare Worker at wsc.theserverless.dev.
npm run build
npx wrangler deploySee CONTRIBUTING.md for development setup, testing, and pull request guidelines.
For substantial changes, please open an issue first.
Matt Might for the original shell scripts
Built with SvelteKit and Svelte 5, deployed on Cloudflare Workers
Logo made with DiffusionBee
Read a UTF-8 file from local disk and analyze it for the same writing style issues as check_text (weasel words, passive voice, hedging, AI tells, and more). Read-only: the file is never modified, and Markdown files are automatically masked so code blocks and tables are not linted as prose. A .wscrc.json config is auto-discovered from the file's directory upward unless config is passed explicitly. Returns the same line-and-column report as check_text, or an error message for unreadable or oversized (over 100,000 characters) files. Use when the text lives on disk; use check_text for text already in the conversation.
Inputs: path (required), config.
Analyze text for writing style issues: weasel words, passive voice, duplicate words, long sentences, nominalizations, hedging, filler adverbs, and research-cited AI tells. Read-only and stateless — text is analyzed in memory, never stored. Returns a plain-text report with each issue's line and column, the matched text, surrounding context, and the reason for AI tells; texts over 100,000 characters return an error message. Use this for text already in the conversation; use check_file for files on disk. It only reports issues — to auto-remove duplicate words, follow up with fix_duplicates.
Inputs: text (required), config, format.
Remove duplicate adjacent words (case-insensitive, including across line breaks) and return the cleaned text plus the list of words that were removed. Read-only with no side effects: the fix is returned in the response, nothing is written anywhere. Use after check_text or check_file reports duplicate words; other issue types are report-only and have no auto-fix.
Inputs: text (required).
Return every detector word/phrase list with its entry count, config key, and sample entries, plus a link to the full browsable library. Read-only, takes no parameters, and returns the same catalog for a given release. Use it to see what the detectors match before tuning a config for check_text or check_file; not needed for ordinary checking.
Inputs: no named parameters.
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
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Scored across 4 tools
Each tool has a clearly distinct purpose: check_file vs check_text differ by input source, fix_duplicates is for fixing a specific issue, and list_word_lists provides reference data. No overlap in functionality.
All tool names follow a consistent verb_noun pattern in snake_case (check_file, check_text, fix_duplicates, list_word_lists), making the API predictable.
With 4 tools, the server is well-scoped for a writing style checker: two input methods for analysis, one fix action, and one list action. Not overly sparse or heavy.
The tool surface covers core checking and duplicate removal, but lacks auto-fix for other issue types (e.g., weasel words, passive voice). Users must manually edit text after reports, which is a minor gap.
DraftCheck: flags AI-writing patterns in prose with fix hints. Deterministic linter, not a detector.
Paste a draft and see the habits that make writing read like it came from an AI model.
Scan manuscript text for AI-slop prose patterns before publishing to Amazon KDP.
Scores whether a page reads as AI-generated and names the tells to fix.