Config is the same across clients — only the file and path differ.
{
"mcpServers": {
"writing-style-checker": {
"args": [
"wsc-mcp"
],
"command": "npx"
}
}
}Are you the author?
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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.
Run this in your terminal to verify the server starts. Then let us know if it worked — your result helps other developers.
npx -y 'wsc-mcp' 2>&1 | head -1 && echo "✓ Server started successfully"
After testing, let us know if it worked:
Five weighted categories — click any category to see the underlying evidence.
No known CVEs.
Checked wsc-mcp against OSV.dev.
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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.

wsc-mcp on npmwsc-lint::warning annotations.wscrc.json filesWSC 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 -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.
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,
... [View full README on GitHub](https://github.com/theserverlessdev/wsc#readme)