slop-cop
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@slop-copfix slop: 'Moving forward, we will utilize our core competencies.'"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
Slop Cop - MCP Server
An MCP server that detects and fixes LLM prose patterns in text. It exposes tools to Claude Code, Claude Desktop, and any MCP-compatible host.
Built on the detection engine and browser app slop-cop by awnist that highlights these patterns in real time.
Setup
Claude Code
claude mcp add slop-cop -- npx -y @0ju1c3/slop-cop@latestClaude Desktop
Add to ~/Library/Application Support/Claude/claude_desktop_config.json (macOS) or %APPDATA%\Claude\claude_desktop_config.json (Windows):
{
"mcpServers": {
"slop-cop": {
"command": "npx",
"args": ["-y", "@0ju1c3/slop-cop@latest"]
}
}
}Restart Claude Desktop after saving.
Prerequisites
Node.js 18+
Related MCP server: MCP Server with Local LLM
Authentication
detect_slop runs entirely locally — no API key, no credentials.
fix_slop and detect_slop_full call the Anthropic API and require ANTHROPIC_API_KEY. Add it to your MCP server env config:
Claude Code:
claude mcp add slop-cop --env ANTHROPIC_API_KEY=sk-ant-... -- npx -y @0ju1c3/slop-cop@latestClaude Desktop / other hosts:
{
"mcpServers": {
"slop-cop": {
"command": "npx",
"args": ["-y", "@0ju1c3/slop-cop@latest"],
"env": { "ANTHROPIC_API_KEY": "sk-ant-..." }
}
}
}Tools
detect_slop
Fast, instant detection using 36 regex and NLP rules. No API call, runs locally.
Use detect_slop on: "It is worth noting that this comprehensive solution
will fundamentally transform the landscape of modern development."Returns a JSON array of violations:
[
{
"ruleId": "important-to-note",
"startIndex": 0,
"endIndex": 18,
"matchedText": "It is worth noting",
"ruleName": "\"It's Important to Note\"",
"category": "rhetorical",
"tip": "Delete the phrase and just say the thing."
},
{
"ruleId": "elevated-register",
"matchedText": "fundamentally transform",
"suggestedChange": "",
...
}
]Best for: auditing prose, feeding violation data into a larger workflow, quick checks inside Claude Code sessions.
detect_slop_full
Runs all 36 client-side rules plus semantic analysis via the Anthropic API, catching the 12 patterns that require language understanding: sycophantic framing, throat-clearing, grandiose stakes, fractal summaries, and others.
Use detect_slop_full on: "And yes, I'll be honest — this challenge will
define the next era of computing for organizations that rise to meet it."The LLM tier catches what regex can't:
false-vulnerability—"And yes, I'll be honest"(performative candour)grandiose-stakes—"define the next era of computing"(inflated significance)
Best for: thorough audits of important prose before publishing.
fix_slop
Detects violations and fixes them in two phases:
Phase 1 — direct splices (instant, free): Violations with a known replacement are applied as exact character-position substitutions — "fosters" → "builds", "moving forward" → deleted, "utilize" → "use" — with no LLM call.
Phase 2 — targeted LLM rewrite (only if needed): Violations that require sentence restructuring (predicate adjectives, negation pivots) are rewritten paragraph-by-paragraph with rule-specific directives.
Use fix_slop on: "Ultimately, this transformative approach not only
streamlines workflows but also fosters a culture of innovation moving forward."Returns: "This approach simplifies workflows and builds a culture of innovation."
Best for: cleaning up LLM-generated drafts before review, automating prose quality in content pipelines.
Use Cases
Passive quality gate in Claude Code
Add to your project's CLAUDE.md:
After generating any prose longer than 3 sentences, run detect_slop
on it and revise flagged violations before responding.This makes slop detection automatic. Claude audits its own output before you see it.
Pre-publish audit
Before publishing a blog post, release notes, documentation, or other content generated with Claude's help:
Use detect_slop_full on the following draft: [paste text]Content pipeline quality check
detect_slop returns structured JSON with ruleId, category, startIndex, endIndex, and suggestedChange. Any script or agent that generates text can pipe it through detect_slop and fail if violation density exceeds a threshold.
Inline editing
Ask Claude to run fix_slop on a specific paragraph you know sounds off. Phase 1 makes deterministic fixes; Phase 2 only fires if restructuring is needed.
Patterns Detected
Client-side — instant (36 rules)
Pattern | What it catches |
Overused Intensifier |
|
Elevated Register |
|
Filler Adverb | Sentence-opening |
"Almost" Hedge |
|
Era Opener |
|
Metaphor Crutch |
|
"It's Important to Note" |
|
"Broader Implications" |
|
False Conclusion |
|
Connector Addiction | Paragraph-opening |
Unnecessary Contrast |
|
Em-Dash Pivot | Excessive |
Negation Pivot |
|
Colon Elaboration | Short setup clause followed by long colon elaboration |
Parenthetical Qualifier | Long parentheticals; comma-bracketed hedges ( |
Question-Then-Answer | Rhetorical question immediately answered in the next sentence |
Hedge Stack | Multiple epistemic hedges in one sentence ( |
Staccato Burst | Three or more consecutive short sentences for dramatic effect |
Listicle Instinct | Bullet or numbered lists with exactly 3, 5, or 7 items |
"Serves As" Dodge |
|
"Not X. Not Y. Just Z." | Consecutive negation sentences building to a reveal |
Anaphora Abuse | Three or more consecutive sentences with the same opener |
Gerund Fragment Litany | Consecutive short sentences starting with gerunds |
"Here's the Kicker" |
|
Pedagogical Aside |
|
"Imagine a World Where" | Hypothetical world-building openers |
Listicle in a Trench Coat | Prose disguising a list via |
Vague Attribution |
|
Bold-First Bullets | Bullet items formatted |
Unicode Decoration |
|
"Despite Its Challenges" |
|
Invented Concept Label |
|
Dramatic Fragment | One-to-four-word standalone paragraph used for emphasis |
Superficial Analysis |
|
False Range | Hollow |
Triple Construction | Exactly three parallel items: |
Semantic — sentence-level (9 rules, via Claude Haiku)
Throat-Clearing Opener · Sycophantic Frame · Balanced Take · Unnecessary Elaboration · Empathy Performance · Pivot Paragraph · Grandiose Stakes · Historical Analogy Stack · False Vulnerability
Semantic — document-level (3 rules, via Claude Haiku)
Dead Metaphor · One-Point Dilution · Fractal Summaries
How It Works
Detection runs in two tiers:
Client-side (36 rules): Regex, structural analysis, and NLP via compromise. Runs entirely locally. It's instant, requires no API calls, and costs nothing.
Semantic (12 rules): API call to claude-haiku-4-5-20251001. Only runs when you call detect_slop_full or when fix_slop needs to restructure a sentence that a direct splice can't fix.
fix_slop applies all deterministic string splices first, at no cost, then calls the LLM only for violations that require sentence restructuring. Many texts are fully fixed in Phase 1 alone.
AI Detection Flow
detect_slop_full and fix_slop call an LLM in two steps:
MCP sampling first. The server calls sampling/createMessage to ask the MCP host to run a model on its behalf, as the MCP spec defines. Claude Code and Claude Desktop don't currently implement this; both return -32601: Method not found. The attempt is kept for forward compatibility with compliant hosts.
Direct Anthropic API as fallback. When sampling fails, the server calls the Anthropic API directly with claude-haiku-4-5-20251001 using ANTHROPIC_API_KEY. If the key is not set, the tool returns a clear error.
Building from Source
git clone https://github.com/0ju1c3/slop-cop
cd slop-cop
npm install
npm run build # compiles TypeScript → build/To use the local build instead of the npm package:
Claude Code:
claude mcp add slop-cop -- node /path/to/slop-cop/build/index.jsClaude Desktop:
{
"mcpServers": {
"slop-cop": {
"command": "node",
"args": ["/path/to/slop-cop/build/index.js"]
}
}
}License
MIT — see LICENSE.
Detection engine derived from slop-cop by awnist, MIT licensed.
Pattern taxonomy based on LLM_PROSE_TELLS.md, Wikipedia: Signs of AI Writing, and tropes.md.
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