mcp-ai-slop-checker

An MCP server that tells your model when its own writing sounds like AI.

claude mcp add ai-slop-checker -- npx -y github:parweb/mcp-ai-slop-checker

Three tools, all deterministic, local and offline: no LLM call, no API key, no network request, no telemetry. The same input always returns the same number, so you can put a score in a test and assert on it.

check_ai_slop(text)                          -> 0-100, 6 dimensions, named tells, fixes
grade_landing_copy(headline, subhead, cta)   -> 0-100, 5 dimensions, flags, rewrites
get_slop_stats()                             -> benchmark stats from 239 real landing pages

Why

Every "AI detector" is a probabilistic classifier that guesses at authorship and gets it wrong on both sides. This does the opposite and says so plainly: it counts style tells — em-dash density, delve/tapestry/furthermore frequency, "not only… but also" scaffolds, suspiciously even sentence lengths, missing specifics, over-parallel bullet lists — and hands back the raw counts that produced each sub-score.

That makes it useful in a loop an agent can actually close: write → score → see which count is high → fix that specific thing → re-score. A classifier's "87% likely AI" gives an agent nothing to act on. "hype": 5 does.

A score is a style measurement, not an authorship claim. stripe.com scores 61 and was obviously written by professionals. Low score means reads generic, never was generated.

Install

Listed in the official MCP Registry as io.github.parweb/ai-slop-checker.

Installs straight from GitHub — not on npm yet, so use the github: spec:

claude mcp add ai-slop-checker -- npx -y github:parweb/mcp-ai-slop-checker

Or in any MCP client config (claude_desktop_config.json, .mcp.json, Cursor, etc.):

{
  "mcpServers": {
    "ai-slop-checker": {
      "command": "npx",
      "args": ["-y", "github:parweb/mcp-ai-slop-checker"]
    }
  }
}

Or install the self-contained MCPB bundle (dependencies included, no install step) from the v1.0.2 release — mcp-ai-slop-checker.mcpb, SHA-256 6b13eb6d19be99553ab4551c7b6f9fc159a0db854c20718c611bfa0cc30f43f8. Rebuild it yourself and compare: ./scripts/build-mcpb.sh.

From source:

git clone https://github.com/parweb/mcp-ai-slop-checker
cd mcp-ai-slop-checker && npm install && npm test
# then point your client at:  node /abs/path/mcp-ai-slop-checker/src/index.js

Node >= 18. One runtime dependency (@modelcontextprotocol/sdk) plus zod.

Tools

check_ai_slop(text)

Scores prose 0-100, where 100 reads human. Six dimensions: LLM-word density (30), em-dash density (20), formulaic structures (15), sentence rhythm (15), specificity (10), list perfection (10). ~200+ characters gives a reliable read.

Real output, trimmed to the parts that matter:

// input: a 74-word paragraph of "In today's fast-paced world… delve… Moreover… seamless…"
{
  "score": 34,
  "verdict": "This sounds AI-generated.",
  "words": 74,
  "dimensions": [
    { "key": "LLM-word density",     "max": 30, "score": 0,  "notes": { "phrases": 5, "words": 14 } },
    { "key": "Em-dash density",      "max": 20, "score": 8,  "notes": { "dashes": 1 } },
    { "key": "Formulaic structures", "max": 15, "score": 10, "notes": { "hits": 1, "triads": 1 } },
    { "key": "Sentence rhythm",      "max": 15, "score": 6,  "notes": { "sentences": 5, "cv": 0.2 

... [View full README on GitHub](https://github.com/parweb/mcp-ai-slop-checker#readme)