glin-profanity-mcp

glin-profanity-mcp

Content moderation and profanity detection MCP server with 19 tools, 24 language support, leetspeak/Unicode obfuscation detection, context-aware analysis, batch processing, and user tracking for AI-powered content safety.

Category
Visit Server

README

<h1 align="center">GLIN PROFANITY</h1>

<p align="center"> <strong>ML-Powered Profanity Detection for the Modern Web</strong> </p>

<!-- Badges Row 1: Package Info --> <p align="center"> <a href="https://www.npmjs.com/package/glin-profanity"><img src="https://img.shields.io/npm/v/glin-profanity?style=flat-square&logo=npm&logoColor=white&label=npm" alt="npm version" /></a> <a href="https://pypi.org/project/glin-profanity/"><img src="https://img.shields.io/pypi/v/glin-profanity?style=flat-square&logo=pypi&logoColor=white&label=pypi" alt="PyPI version" /></a> <a href="https://www.npmjs.com/package/glin-profanity"><img src="https://img.shields.io/npm/dm/glin-profanity?style=flat-square&logo=npm&logoColor=white&label=npm%20downloads" alt="npm downloads" /></a> <a href="https://pepy.tech/projects/glin-profanity"><img src="https://img.shields.io/pepy/dt/glin-profanity?style=flat-square&logo=pypi&logoColor=white&label=pypi%20downloads" alt="PyPI downloads" /></a> </p>

<!-- Badges Row 2: Quality & Status --> <p align="center"> <a href="https://github.com/GLINCKER/glin-profanity/actions/workflows/ci.yml"><img src="https://img.shields.io/github/actions/workflow/status/GLINCKER/glin-profanity/ci.yml?style=flat-square&logo=github&label=CI" alt="CI Status" /></a> <a href="https://bundlephobia.com/package/glin-profanity"><img src="https://img.shields.io/bundlephobia/minzip/glin-profanity?style=flat-square&logo=webpack&logoColor=white&label=bundle%20size" alt="Bundle Size" /></a> <a href="https://github.com/GLINCKER/glin-profanity/blob/main/LICENSE"><img src="https://img.shields.io/github/license/GLINCKER/glin-profanity?style=flat-square&label=license" alt="License" /></a> <a href="https://www.typescriptlang.org/"><img src="https://img.shields.io/badge/TypeScript-Ready-3178C6?style=flat-square&logo=typescript&logoColor=white" alt="TypeScript" /></a> </p>

<!-- Badges Row 3: Community --> <p align="center"> <a href="https://github.com/GLINCKER/glin-profanity/stargazers"><img src="https://img.shields.io/github/stars/GLINCKER/glin-profanity?style=flat-square&logo=github&label=stars" alt="GitHub Stars" /></a> <a href="https://github.com/GLINCKER/glin-profanity/network/members"><img src="https://img.shields.io/github/forks/GLINCKER/glin-profanity?style=flat-square&logo=github&label=forks" alt="GitHub Forks" /></a> <a href="https://github.com/GLINCKER/glin-profanity/issues"><img src="https://img.shields.io/github/issues/GLINCKER/glin-profanity?style=flat-square&logo=github&label=issues" alt="GitHub Issues" /></a> <a href="https://github.com/GLINCKER/glin-profanity/graphs/contributors"><img src="https://img.shields.io/github/contributors/GLINCKER/glin-profanity?style=flat-square&logo=github&label=contributors" alt="Contributors" /></a> </p>

<!-- Hero Image --> <p align="center"> <a href="https://www.glincker.com/tools/glin-profanity" target="_blank"> <img src="./og-image.png" alt="Glin Profanity - ML-Powered Profanity Detection" width="800" /> </a> </p>

<p align="center"> <a href="https://www.glincker.com/tools/glin-profanity"><img src="https://img.shields.io/badge/Try_Live_Demo-online-blue?style=for-the-badge&logo=vercel&logoColor=white" alt="Live Demo" /></a> </p>


πŸ“¦ Packages

This monorepo maintains the following packages:

Package Version Description
glin-profanity npm Core profanity filter for JavaScript/TypeScript
glin-profanity PyPI Core profanity filter for Python
glin-profanity-mcp npm MCP server for AI assistants (Claude, Cursor, etc.)
openclaw-profanity npm Plugin for OpenClaw/Moltbot AI agents

Why Glin Profanity?

Most profanity filters are trivially bypassed. Users type f*ck, sh1t, or fΥ½ck (with Cyrillic characters) and walk right through. Glin Profanity doesn't just check against a word listβ€”it understands evasion tactics.

β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”
β”‚                           GLIN PROFANITY v3                                 β”‚
β”œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€
β”‚                                                                             β”‚
β”‚   Input Text ──►  Unicode       ──►  Leetspeak    ──►  Dictionary  ──► ML  β”‚
β”‚                   Normalization      Detection         Matching        Checkβ”‚
│                   (homoglyphs)       (f4ck→fuck)       (23 langs)     (opt) │
β”‚                                                                             β”‚
β”‚   "fΥ½ck"     ──►  "fuck"        ──►  "fuck"       ──►  MATCH       ──► βœ“   β”‚
β”‚                                                                             β”‚
β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜

Performance Benchmarks

Tested on Node.js 20, M1 MacBook Pro, single-threaded:

Operation Glin Profanity bad-words leo-profanity obscenity
Simple check 21M ops/sec 890K ops/sec 1.2M ops/sec 650K ops/sec
With leetspeak 8.5M ops/sec N/A N/A N/A
Multi-language (3) 18M ops/sec N/A 400K ops/sec N/A
Unicode normalization 15M ops/sec N/A N/A N/A

Feature Comparison

Feature Glin Profanity bad-words leo-profanity obscenity
Leetspeak detection (f4ck, sh1t) Yes No No Partial
Unicode homoglyph detection Yes No No No
ML toxicity detection Yes (TensorFlow.js) No No No
Multi-language support 23 languages English only 14 languages English only
Result caching (LRU) Yes No No No
Severity levels Yes No No No
React hook Yes No No No
Python package Yes No No No
TypeScript types Full Partial Partial Full
Bundle size (minified) 12KB + dictionaries 8KB 15KB 6KB
Active maintenance Yes Limited Limited Limited

Installation

JavaScript/TypeScript

npm install glin-profanity

Python

pip install glin-profanity

Quick Start

JavaScript

import { checkProfanity, Filter } from 'glin-profanity';

// Simple check
const result = checkProfanity("This is f4ck1ng bad", {
  detectLeetspeak: true,
  languages: ['english']
});

result.containsProfanity  // true
result.profaneWords       // ['fucking']

// With replacement
const filter = new Filter({
  replaceWith: '***',
  detectLeetspeak: true
});
filter.checkProfanity("sh1t happens").processedText  // "*** happens"

Python

from glin_profanity import Filter

filter = Filter({"languages": ["english"], "replace_with": "***"})

filter.is_profane("damn this")           # True
filter.check_profanity("damn this")      # Full result object

React

import { useProfanityChecker } from 'glin-profanity';

function ChatInput() {
  const { result, checkText } = useProfanityChecker({
    detectLeetspeak: true
  });

  return (
    <input onChange={(e) => checkText(e.target.value)} />
    {result?.containsProfanity && <span>Clean up your language</span>}
  );
}

Architecture

flowchart LR
    subgraph Input
        A[Raw Text]
    end

    subgraph Processing
        B[Unicode Normalizer]
        C[Leetspeak Decoder]
        D[Word Tokenizer]
    end

    subgraph Detection
        E[Dictionary Matcher]
        F[Fuzzy Matcher]
        G[ML Toxicity Model]
    end

    subgraph Output
        H[Result Object]
    end

    A --> B --> C --> D
    D --> E --> H
    D --> F --> H
    D -.->|Optional| G -.-> H

Detection Capabilities

Leetspeak Detection

const filter = new Filter({
  detectLeetspeak: true,
  leetspeakLevel: 'aggressive'  // basic | moderate | aggressive
});

filter.isProfane('f4ck');     // true
filter.isProfane('5h1t');     // true
filter.isProfane('@$$');      // true
filter.isProfane('ph.u" "ck'); // true (aggressive mode)

Unicode Homoglyph Detection

const filter = new Filter({ normalizeUnicode: true });

filter.isProfane('fΥ½ck');   // true (Armenian 'Υ½' β†’ 'u')
filter.isProfane('shΡ–t');   // true (Cyrillic 'Ρ–' β†’ 'i')
filter.isProfane('Ζ’uck');   // true (Latin 'Ζ’' β†’ 'f')

ML-Powered Detection

import { loadToxicityModel, checkToxicity } from 'glin-profanity/ml';

await loadToxicityModel({ threshold: 0.9 });

const result = await checkToxicity("You're the worst player ever");
// { toxic: true, categories: { toxicity: 0.92, insult: 0.87, ... } }

Supported Languages

23 languages with curated dictionaries:

Arabic Chinese Czech Danish
Dutch English Esperanto Finnish
French German Hindi Hungarian
Italian Japanese Korean Norwegian
Persian Polish Portuguese Russian
Spanish Swedish Thai Turkish

Documentation

Document Description
Getting Started Installation and basic usage
API Reference Complete API documentation
Framework Examples React, Vue, Angular, Express, Next.js
Advanced Features Leetspeak, Unicode, ML, caching
ML Guide TensorFlow.js integration
Changelog Version history

Local Testing Interface

Run the interactive playground locally to test profanity detection:

# Clone the repo
git clone https://github.com/GLINCKER/glin-profanity.git
cd glin-profanity/packages/js

# Install dependencies
npm install

# Start the local testing server
npm run dev:playground

Open http://localhost:4000 to access the testing interface with:

  • Real-time profanity detection
  • Toggle leetspeak, Unicode normalization, ML detection
  • Multi-language selection
  • Visual results with severity indicators

Use Cases

Application How Glin Profanity Helps
Chat platforms Real-time message filtering with React hook
Gaming Detect obfuscated profanity in player names/chat
Social media Scale moderation with ML-powered detection
Education Maintain safe learning environments
Enterprise Filter internal communications
AI/ML pipelines Clean training data before model ingestion

MCP Server for AI Assistants

Glin Profanity includes an MCP (Model Context Protocol) server that enables AI assistants like Claude Desktop, Cursor, Windsurf, and other MCP-compatible tools to use profanity detection as a native tool.

Quick Setup

Claude Desktop (~/Library/Application Support/Claude/claude_desktop_config.json):

{
  "mcpServers": {
    "glin-profanity": {
      "command": "npx",
      "args": ["-y", "glin-profanity-mcp"]
    }
  }
}

Cursor (.cursor/mcp.json):

{
  "mcpServers": {
    "glin-profanity": {
      "command": "npx",
      "args": ["-y", "glin-profanity-mcp"]
    }
  }
}

Available Tools (19)

Tool Description
check_profanity Check text for profanity with detailed results
censor_text Censor profanity with configurable replacement
analyze_context Context-aware analysis with domain whitelists
batch_check Check multiple texts in one operation
validate_content Content validation with safety scoring (0-100)
detect_obfuscation Detect leetspeak and Unicode tricks
get_supported_languages List all 24 supported languages
explain_match Explain why text was flagged with reasoning
suggest_alternatives Suggest clean alternatives for profane content
analyze_corpus Analyze up to 500 texts for moderation stats
compare_strictness Compare results across strictness levels
create_regex_pattern Generate regex patterns for custom detection
track_user_message Track user messages for repeat offender detection
get_user_profile Get moderation profile for a specific user
get_high_risk_users List users with high violation rates
reset_user_profile Reset a user's moderation history
stream_check Real-time streaming profanity check
stream_batch Stream multiple texts with live results
get_stream_stats Get streaming session statistics

Plus 4 workflow prompts and 5 reference resources for guided AI interactions.

Example Prompts for AI Assistants

"Check this user comment for profanity using glin-profanity"
"Validate this blog post content with high strictness"
"Batch check these 50 messages for any inappropriate content"
"Analyze this medical text with the medical domain context"

See the full MCP documentation for setup instructions and examples.


Roadmap

See our ROADMAP.md for planned features including:

  • Streaming support for real-time chat
  • OpenAI function calling integration
  • Image OCR for profanity in images
  • Edge deployment (Cloudflare Workers, Vercel Edge)
  • More framework integrations

License

MIT License - free for personal and commercial use.

Enterprise licensing with SLA and support available from GLINCKER.


Contributing

See CONTRIBUTING.md for guidelines. We welcome:

  • Bug reports and fixes
  • New language dictionaries
  • Performance improvements
  • Documentation updates

Star History

<a href="https://star-history.com/#GLINCKER/glin-profanity&Date"> <picture> <source media="(prefers-color-scheme: dark)" srcset="https://api.star-history.com/svg?repos=GLINCKER/glin-profanity&type=Date&theme=dark" /> <source media="(prefers-color-scheme: light)" srcset="https://api.star-history.com/svg?repos=GLINCKER/glin-profanity&type=Date" /> <img alt="Star History Chart" src="https://api.star-history.com/svg?repos=GLINCKER/glin-profanity&type=Date" /> </picture> </a>


<div align="center">

Live Demo Β· NPM Β· PyPI Β· GitHub

<br />

<a href="https://github.com/GLINCKER/glin-profanity/stargazers"> <img src="https://img.shields.io/github/stars/GLINCKER/glin-profanity?style=social" alt="Star on GitHub" /> </a>

<br /><br />

<sub>Built by <a href="https://glincker.com">GLINCKER</a> Β· Part of the <a href="https://github.com/GLINCKER">GLINR</a> ecosystem</sub>

</div>

Recommended Servers

playwright-mcp

playwright-mcp

A Model Context Protocol server that enables LLMs to interact with web pages through structured accessibility snapshots without requiring vision models or screenshots.

Official
Featured
TypeScript
Magic Component Platform (MCP)

Magic Component Platform (MCP)

An AI-powered tool that generates modern UI components from natural language descriptions, integrating with popular IDEs to streamline UI development workflow.

Official
Featured
Local
TypeScript
Audiense Insights MCP Server

Audiense Insights MCP Server

Enables interaction with Audiense Insights accounts via the Model Context Protocol, facilitating the extraction and analysis of marketing insights and audience data including demographics, behavior, and influencer engagement.

Official
Featured
Local
TypeScript
VeyraX MCP

VeyraX MCP

Single MCP tool to connect all your favorite tools: Gmail, Calendar and 40 more.

Official
Featured
Local
graphlit-mcp-server

graphlit-mcp-server

The Model Context Protocol (MCP) Server enables integration between MCP clients and the Graphlit service. Ingest anything from Slack to Gmail to podcast feeds, in addition to web crawling, into a Graphlit project - and then retrieve relevant contents from the MCP client.

Official
Featured
TypeScript
Kagi MCP Server

Kagi MCP Server

An MCP server that integrates Kagi search capabilities with Claude AI, enabling Claude to perform real-time web searches when answering questions that require up-to-date information.

Official
Featured
Python
E2B

E2B

Using MCP to run code via e2b.

Official
Featured
Neon Database

Neon Database

MCP server for interacting with Neon Management API and databases

Official
Featured
Exa Search

Exa Search

A Model Context Protocol (MCP) server lets AI assistants like Claude use the Exa AI Search API for web searches. This setup allows AI models to get real-time web information in a safe and controlled way.

Official
Featured
Qdrant Server

Qdrant Server

This repository is an example of how to create a MCP server for Qdrant, a vector search engine.

Official
Featured