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glincker

glin-profanity-mcp

by glincker

batch_check

Check up to 100 texts for profanity in one request, with multi-language support and options for leetspeak and Unicode obfuscation detection.

Instructions

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

Input Schema

NameRequiredDescriptionDefault
textsYesArray of texts to check
languagesNoLanguages to check against. Defaults to all languages.
caseSensitiveNoWhether matching should be case-sensitive. Default: false
detectLeetspeakNoDetect leetspeak obfuscation (e.g., 'f4ck'). Default: false
normalizeUnicodeNoNormalize Unicode homoglyphs. Default: true
returnOnlyFlaggedNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.2

TDQS

B3.2/5.0
Behavior2/5

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

No annotations are provided, so the description carries the full behavioral burden. It discloses the batch limit of 100 texts but says nothing about return shape, error handling, per-text results, rate limits, or whether any texts are skipped or combined during checking.

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

Conciseness5/5

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

The description is a single front-loaded sentence with no wasted wording. It communicates purpose and an important limit immediately.

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?

With no annotations, no output schema, and six parameters, the description is too thin for an agent to fully understand what a batch profanity check returns or how per-text results are represented. The max-100 rule is useful, but the broader behavioral contract is largely missing.

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 high at 83%, so the schema already documents nearly all parameters, including the texts array and language options. The description adds no parameter-level meaning beyond the max-100 batch size, which the schema also captures.

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?

The description clearly states a specific verb and resource: check multiple texts for profanity in one operation. It implies a batch-oriented counterpart to single-text checks, though it does not explicitly distinguish itself from siblings like check_profanity or stream_batch.

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

Usage Guidelines3/5

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

The tool's usage is implied by 'multiple texts' and 'single operation,' and the max-100 limit gives useful context. However, it does not explicitly say when to use this versus check_profanity, stream_batch, or validate_content, nor does it state any exclusions or prerequisites.

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