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glin-profanity-mcp

by glincker

detect_obfuscation

Analyzes text to detect obfuscation techniques such as leetspeak and Unicode homoglyphs, helping identify disguised content before it bypasses filters.

Instructions

Detect text obfuscation techniques like leetspeak and Unicode homoglyphs

Input Schema

NameRequiredDescriptionDefault
textYesThe text to analyze

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.2

TDQS

C2.9/5.0
Behavior2/5

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

With no annotations, the description carries the full disclosure burden, yet it says nothing about what the detection returns, whether matches include spans or confidence scores, thresholds, or size limits. Only the implied read-only nature of 'detect' gives the agent any behavioral signal.

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?

One front-loaded sentence with zero filler, naming the action and the technique categories up front. It is efficient, though its brevity borders on under-specification rather than tightness.

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

Completeness3/5

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

For a simple one-parameter analysis tool this is the minimum viable: the caller knows what to pass in. But with no annotations and no output schema, the description should say something about the shape of the detection result, which it omits.

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?

There is a single parameter with 100% schema description coverage, so the schema already documents 'text: the text to analyze' fully. The description adds no format, encoding, or length constraints beyond that, matching the baseline-3 case for high coverage.

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 ('detect') and a specific resource ('text obfuscation techniques'), and the examples leetspeak/homoglyphs carve out a distinct domain relative to siblings like check_profanity or scan_pii. It stops short of explicitly naming a sibling it differs from, so it lands at 4 rather than 5.

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 guidance on when to reach for this tool versus alternatives such as validate_content, analyze_context, or check_prompt_injection, and no mention of prerequisites or exclusions. The agent must infer the call context entirely from the tool name.

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