WSC - Writing Style Checker
Flag AI tells, weasel words, passive voice, duplicate words, long sentences, nominalizations, hedging, and filler adverbs
README
Writing Style Checker
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.

Features
- 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-mcpon npm - CLI - Check files from the command line via
wsc-lint - GitHub Action - Run checks in CI with
::warningannotations - Configurable - Customize detectors with
.wscrc.jsonfiles
What WSC is (and isn't)
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.
Detection Rules
| 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 -ed + irregular) |
| 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 (delve, rich tapestry, It's not just X — it's Y) |
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.
Configuration
Create a .wscrc.json to customize detectors. All tools (API, MCP, CLI) support it.
{
"$schema": "https://wsc.theserverless.dev/schema.json",
"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.
API Usage
POST /api/check
Analyze 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/check
Returns API documentation as JSON.
GET /api/detectors
Returns the list of all 8 detectors with descriptions, configurability, and word counts.
GET /health
Runs a smoke test with known text and returns {"status":"healthy"} or 503.
Limits: Max 100,000 characters per request. CORS enabled for all origins.
MCP Server
The Writing Style Checker is available as an MCP server, letting AI assistants check your writing directly.
Tools
| Tool | Description |
|---|---|
check_text |
Analyze text for all 8 writing style issues. Accepts optional config. |
fix_duplicates |
Remove duplicate adjacent words and return cleaned text |
list_word_lists |
Return info about all detector word lists |
check_file |
(Local only) Read a file from disk and analyze it. Auto-discovers .wscrc.json. |
Remote MCP Server
Connect any MCP client to the hosted server - no installation required.
{
"mcpServers": {
"writing-style-checker": {
"type": "url",
"url": "https://wsc.theserverless.dev/mcp"
}
}
}
Local MCP Server (stdio)
Install via npm for local usage. Includes check_file for analyzing files on disk with auto-discovery of .wscrc.json.
npx wsc-mcp
Claude Desktop / Claude Code config:
{
"mcpServers": {
"writing-style-checker": {
"command": "npx",
"args": ["wsc-mcp"]
}
}
}
See the wsc-mcp npm page for full documentation.
CLI
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 init
See the wsc-lint README for full documentation.
GitHub Action
- uses: theserverlessdev/wsc@v1
with:
files: '**/*.md'
max-warnings: 20
| Input | Default | Description |
|---|---|---|
files |
**/*.md |
Glob pattern for files to check |
config |
— | Path to .wscrc.json config file |
max-warnings |
unlimited | Max warnings before failing |
only-changed |
false |
Only check files changed in this PR |
Privacy
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.
Project Structure
.
├── 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 configuration
Local Development
git clone https://github.com/theserverlessdev/wsc.git
cd wsc
npm install
npm run dev
Visit http://localhost:5173. The API is at /api/check, MCP at /mcp, health at /health.
Commands
| Command | Description |
|---|---|
npm run dev |
Start dev server |
npm run build |
Build for production |
npm run check |
Type check with svelte-check |
npm test |
Run all 341 tests |
npm run test:coverage |
Coverage report |
Deployment
Deployed as a Cloudflare Worker at wsc.theserverless.dev.
npm run build
npx wrangler deploy
Contributing
See CONTRIBUTING.md for development setup, testing, and pull request guidelines.
For substantial changes, please open an issue first.
Acknowledgements
- Matt Might for the original shell scripts
- Built with SvelteKit and Svelte 5, deployed on Cloudflare Workers
- Logo made with DiffusionBee
License
推荐服务器
Baidu Map
百度地图核心API现已全面兼容MCP协议,是国内首家兼容MCP协议的地图服务商。
Playwright MCP Server
一个模型上下文协议服务器,它使大型语言模型能够通过结构化的可访问性快照与网页进行交互,而无需视觉模型或屏幕截图。
Magic Component Platform (MCP)
一个由人工智能驱动的工具,可以从自然语言描述生成现代化的用户界面组件,并与流行的集成开发环境(IDE)集成,从而简化用户界面开发流程。
Audiense Insights MCP Server
通过模型上下文协议启用与 Audiense Insights 账户的交互,从而促进营销洞察和受众数据的提取和分析,包括人口统计信息、行为和影响者互动。
VeyraX
一个单一的 MCP 工具,连接你所有喜爱的工具:Gmail、日历以及其他 40 多个工具。
graphlit-mcp-server
模型上下文协议 (MCP) 服务器实现了 MCP 客户端与 Graphlit 服务之间的集成。 除了网络爬取之外,还可以将任何内容(从 Slack 到 Gmail 再到播客订阅源)导入到 Graphlit 项目中,然后从 MCP 客户端检索相关内容。
Kagi MCP Server
一个 MCP 服务器,集成了 Kagi 搜索功能和 Claude AI,使 Claude 能够在回答需要最新信息的问题时执行实时网络搜索。
e2b-mcp-server
使用 MCP 通过 e2b 运行代码。
Neon MCP Server
用于与 Neon 管理 API 和数据库交互的 MCP 服务器
Exa MCP Server
模型上下文协议(MCP)服务器允许像 Claude 这样的 AI 助手使用 Exa AI 搜索 API 进行网络搜索。这种设置允许 AI 模型以安全和受控的方式获取实时的网络信息。