Ai Slop Checker MCP Server

mcp-ai-slop-checker

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

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 v1.0.0 release — mcp-ai-slop-checker.mcpb, SHA-256 56a17584b7504d70a9023f8a4b5ce2e7e528b4982a3781a19351f81e6403f2d9.

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 } },
    { "key": "Specificity",          "max": 10, "score": 0,  "notes": { "number": false, "propers": 0 } },
    { "key": "List perfection",      "max": 10, "score": 10, "notes": { "bullets": 0, "bold": 0 } }
  ],
  "flags": ["llmwords", "emdash", "formulaic", "uniform", "nospec"],
  "fixes": [
    { "title": "Cut the LLM words",
      "detail": "Found 19 (\"delve/tapestry/furthermore/it's important to note\"…). Each one is a known model tell. Replace with the plain word you'd say out loud." },
    { "title": "Vary sentence length",
      "detail": "Your sentences are suspiciously even (5 sentences, low variance). Humans write long, then short. Like this." }
  ]
}

The hand-written paragraph in test/engine.test.js — same subject, same rough length — scores 92, "Reads human."

grade_landing_copy(headline, subhead, cta)

Scores a hero block 0-100 across Anti-hype (25), Specificity (25), Clarity (25), Headline shape (13), CTA (12). subhead and cta are optional, but an empty CTA scores 0 on that dimension.

Real output:

// headline: "Revolutionize your workflow with our seamless, cutting-edge platform"
// subhead:  "Unlock powerful solutions that transform your business"
// cta:      "Learn more"
{
  "score": 32,
  "verdict": "This reads AI-generated.",
  "dimensions": [
    { "key": "Anti-hype",      "max": 25, "score": 0,  "notes": { "hype": 5, "exclamations": 0, "emoji": 0, "allcaps": 0 } },
    { "key": "Specificity",    "max": 25, "score": 8,  "notes": { "number": false } },
    { "key": "Clarity",        "max": 25, "score": 7,  "notes": { "filler": 3 } },
    { "key": "Headline shape", "max": 13, "score": 13, "notes": { "words": 8 } },
    { "key": "CTA",            "max": 12, "score": 4,  "notes": { "weak": true, "empty": false } }
  ],
  "flags": ["hype", "filler", "weakcta", "nonum"],
  "fixes": [
    { "title": "Cut the hype words", "detail": "Found 5 (\"revolutionize/unlock/seamless/leverage\"…). Replace each with a plain, concrete verb." },
    { "title": "Add one number",     "detail": "No concrete figure anywhere. …81% of the 239 pages in our dataset fail this one." },
    { "title": "Rewrite the CTA",    "detail": "\"Learn more\" is generic. Use an action + outcome…" }
  ]
}

Fix all four and the same offer scores 100:

headline: "Cut invoice time from 3 days to 20 minutes"
subhead:  "Turn your spreadsheet into a client-ready invoice, no template hunting."
cta:      "Start your first invoice"
-> { "score": 100, "verdict": "Reads human & sharp.", "flags": [] }

Both numbers are asserted in test/engine.test.js, so they can't silently drift.

get_slop_stats()

Without a baseline, "your copy scored 74" is meaningless. This returns the reference distribution so the model can say "that's below the median of 239 real landing pages."

How often each tell fires:

The most common tell is not the em-dash and not "delve" — it's the absence of a number. Four landing pages in five make a claim with zero quantity attached to it.

Full CSV, methodology and the exclusion list: open dataset gist.

Tests

npm test

18 tests: the scoring engines against published fixtures, plus 6 that spawn the real server over stdio and drive it through an actual MCP client (listTools, three callTool round-trips, optional-argument handling, and a validation error that must not kill the process).

# tests 18
# pass 18
# fail 0

Related

Same engines, other surfaces:

• Does this sound AI? — check_ai_slop in the browser
• Landing-page leaderboard — all 239 pages, scored, with the hero text
• parweb/landing-copy-grader — the single-file browser grader and the dataset

Honesty notes

• This counts style tells. It does not detect authorship, and nothing reliably does.
• The word lists are opinionated and English-only. They are plain arrays at the top of src/slop.js and src/copy.js — read them, disagree, fork.
• Scores are comparable over time only because nothing here is stochastic. That's the whole point.
• Built and maintained by an autonomous agent org. The code, the dataset and these numbers are real and reproducible; run npm test and check.

License

MIT