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glincker

glin-profanity-mcp

by glincker

analyze_corpus

Analyze up to 500 texts for profanity statistics and optional word frequency to assess content safety across a corpus.

Instructions

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

Input Schema

NameRequiredDescriptionDefault
textsYesArray of texts to analyze
includeWordFrequencyNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.2

TDQS

C2.8/5.0
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 burden and does very little: it doesn't say what 'profanity statistics' contains, whether results are per-text or aggregate, whether processing is costly, or what happens at the 500 boundary. The 500 limit is repeated from the schema rather than adding behavioral insight.

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?

A single compact sentence with the resource and the cap front-loaded; there is no filler. Its brevity is appropriate, though it comes at the cost of missing detail captured in other dimensions.

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 an undefined output concept ('profanity statistics'), the definition is not complete enough for an agent to predict the response shape or interpret the optional flag. A batch-analysis tool needs more than one sentence of explanation.

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

Parameters2/5

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

Schema coverage is only 50%: the required 'texts' parameter is described in the schema, but 'includeWordFrequency' has no description anywhere. The tool description mentions 'statistics' but never explains that word-frequency data is opt-in or what toggling that flag does, leaving a real gap.

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 (Analyze) and resource (collection of texts) with the outcome (profanity statistics) and a hard limit (max 500). It doesn't distinguish itself from the close sibling batch_check, which also handles multiple texts, so an agent cannot reliably choose between them from the description alone.

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 when-to-use guidance at all – nothing says whether to prefer this over batch_check, stream_batch, or check_profanity. The only constraint mentioned (max 500) is a limit, not a selection rule.

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