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glincker

glin-profanity-mcp

by glincker

censor_text

Replace profane words in text with a chosen character across 24 languages, with options for leetspeak detection, Unicode normalization, and keeping the first letter visible.

Instructions

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

Input Schema

NameRequiredDescriptionDefault
textYesThe text to censor
languagesNoLanguages to check against. Defaults to all languages.
replaceWithNoReplacement character. Default: '*'
caseSensitiveNoWhether matching should be case-sensitive. Default: false
detectLeetspeakNoDetect leetspeak obfuscation (e.g., 'f4ck'). Default: false
normalizeUnicodeNoNormalize Unicode homoglyphs. Default: true
preserveFirstLetterNoKeep first letter (e.g., 'f***')

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.2

TDQS

B3.1/5.0
Behavior3/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries the full burden; it does disclose the core behavior (matched words are replaced rather than merely flagged), which is the key thing an agent needs. It does not say what the response contains (censored string, match counts, modified vs. original), nor whether the input text is mutated in place, leaving meaningful gaps for a write-style tool.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

A single front-loaded sentence with no filler. Slight redundancy in restating the replacement-character mechanic, which the replaceWith parameter already documents, keeps it from a 5.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Seven parameters, no annotations, and no output schema: the description is one sentence and says nothing about the return value, how match counts or unchanged text are reported, or how the boolean toggles interact. For a tool with this much surface area and zero structured behavioral metadata, it is underspecified.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100% and every one of the 6 optional parameters carries its own default and explanation, so the schema does the heavy lifting. The description's mention of 'replacement character' maps to replaceWith but adds nothing beyond the schema text; baseline 3 applies.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb ('censor') plus resource ('text') and the mechanism ('replacing matched words with a replacement character'). It implicitly distinguishes itself from detection-only siblings like check_profanity, but never names them, so an agent must infer the distinction.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

There is no explicit when-to-use statement and no mention of alternatives, even though the sibling set contains close calls (check_profanity for detection, redact_pii for PII, detect_obfuscation for leetspeak). The agent must infer that this is the mutating counterpart to those detection tools from the verb 'censor' alone.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.