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Glama
glin-profanity-mcp

glin-profanity-mcp

by glincker

Server Configuration

Describes the environment variables required to run the server.

NameRequiredDescriptionDefault

No arguments

Instructions

Guidance the server publishes about itself, which clients place ahead of the tool catalog so the model reads it before choosing anything.

This server publishes no instructions, or was last inspected before Glama recorded them.

Capabilities

Features and capabilities supported by this server

Protocol revision2025-11-25

CapabilityDetails
tools
{
  "listChanged": true
}
prompts
{
  "listChanged": true
}
resources
{
  "listChanged": true
}

Tools

Functions exposed to the LLM to take actions

NameDescription
check_profanityB

Check text for profanity and get detailed results including matched words, severity, and positions

censor_textB

Censor profanity in text by replacing matched words with a replacement character

analyze_contextC

Analyze text with context awareness - considers domain-specific whitelists

batch_checkB

Check multiple texts for profanity in a single operation (max 100)

get_supported_languagesA

Get the list of all 24 supported languages

validate_contentC

Validate content with a safety score (0-100) and action recommendation

detect_obfuscationC

Detect text obfuscation techniques like leetspeak and Unicode homoglyphs

explain_matchC

Explain why a word was flagged with detailed reasoning

suggest_alternativesC

Suggest clean alternatives for profane content

analyze_corpusC

Analyze a collection of texts for profanity statistics (max 500)

compare_strictnessB

Compare detection results across different strictness levels

create_regex_patternC

Generate regex patterns for custom profanity detection

track_user_messageB

Track a user message and update their risk profile - enables pattern detection across messages

get_user_profileC

Get a user's moderation profile and risk assessment

get_high_risk_usersB

Get list of users with high risk scores for review

reset_user_profileB

Reset a user's moderation profile (use after appeals or timeouts)

stream_checkC

Add message to real-time processing queue and get instant result - optimized for chat streams

stream_batchC

Process multiple messages in real-time batch mode - for high-throughput scenarios

get_stream_statsB

Get real-time streaming statistics and performance metrics

check_prompt_injectionA

Scan text for prompt injection attacks using rule-based pattern matching. Returns a risk score (0-1), a decision (ALLOW / HITL / BLOCK), matched categories, and per-match position details. Custom patterns: max 20 patterns, each regex max 200 chars, 50 ms execution budget per pattern.

scan_secretsA

Scan text for leaked credentials, API keys, tokens, and other secrets. Returns a full ScanResult with per-match details including pattern id, family, and character positions.

scan_piiA

Scan text for Personally Identifiable Information (email, phone, SSN, credit card, IBAN, IP, MAC, passport, date of birth, etc). Returns a full ScanResult with per-match details. Use redact_pii for a reversible vault-backed redaction.

redact_piiA

Redact PII from text using a server-side vault for reversible round-trip. Returns sanitized text with [REDACTED__N] placeholders, a vaultId to use for restoration, and a list of placeholder+type pairs. Original values never leave the server. Call restore_pii with the same vaultId to get originals back.

restore_piiA

Restore PII placeholders in text back to their original values using a vault session created by redact_pii. The vault is maintained server-side — originals were never sent to the AI client.

Prompts

Interactive templates invoked by user choice

NameDescription
content_moderationStep-by-step content moderation workflow
content_cleanupClean up content containing profanity
audit_reportGenerate a comprehensive audit report
filter_tuningTune filter settings for your use case
user_reviewReview a user's moderation history and risk profile

Resources

Contextual data attached and managed by the client

NameDescription
languages
config-examples
severity-levels
domain-whitelists
detection-guide
docs-index
docs-installation
docs-getting-started
docs-configuration
docs-api-reference
docs-faq
docs-integrations-overview
docs-integrations-openai
docs-integrations-langchain
docs-integrations-vercel
docs-integrations-semantic
docs-examples
docs-testing
docs-deployment
docs-security
docs-features
docs-mcp-guide
docs-openclaw

TDQS

B3.2/5.0

Scored across 24 tools

Disambiguation4/5

Most tools target distinct moderation operations, but check_profanity, analyze_context, and validate_content all assess text safety with subtle differences, while batch_check, stream_check, and stream_batch have overlapping batch/stream semantics. Descriptions help distinguish them, but an agent could misselect without careful reading.

Naming Consistency4/5

The set is mostly consistent snake_case verb_noun naming (check_profanity, censor_text, detect_obfuscation, redact_pii). Minor deviations like batch_check, stream_check, and stream_batch reverse or blur the verb pattern but remain readable.

Tool Count3/5

At 24 tools the server is heavy for a profanity-checking MCP, expanding into PII, secrets, prompt injection, streaming, and user risk profiles. Each tool seems purposeful, but the surface is broad enough to feel borderline over-scoped for the core domain.

Completeness4/5

Coverage is broad: detection, censorship, alternatives, context, validation, obfuscation, streaming, languages, user risk profiles, prompt injection, secrets, and reversible PII redaction. Minor gaps exist around managing custom regex patterns and redacting secrets, but core moderation workflows are well supported.

Maintenance

ActivityMaintained
ResponsivenessSyncing