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Finekot API

profanity_filter

Finds and masks profanity in en, ru, uk, de, es, fr (auto-detects across all of them unless language is set). Catches basic obfuscation: latin look-alikes for Cyrillic letters, repeated letters, stray asterisks/digits inside a word. Up to 50 000 chars. Cost €0.0005 per request, charged to the API key in Authorization: Bearer. Parameters — text: text to check; language: auto (default, all 6) | comma-separated subset; mode: mask (default) | detect; mask_char: single character (default *).

Input Schema

NameRequiredDescriptionDefault
modeNo
textNo
languageNo
mask_charNo

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.5/5.0
Behavior5/5

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

With no annotations supplied, the description carries the full burden and does so well: it discloses the €0.0005 per-request cost, that billing goes to the API key in the Authorization: Bearer header, the 50,000-character input ceiling, and the obfuscation patterns it recognizes. Those are exactly the non-obvious traits an agent needs before calling a paid external service.

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?

Purpose, language scope, and obfuscation coverage are front-loaded, and the trailing 'Parameters —' segment is a compact enumeration with no filler. The single dense paragraph is slightly harder to scan than a short list would be, but every sentence carries information.

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

Completeness5/5

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

The tool is a straightforward 4-param text transform with an output schema, so return values need no explanation, and the description still covers language behavior, mode defaults, cost, auth, and size limits. Nothing an agent needs to invoke it correctly is missing.

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

Parameters5/5

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

Schema description coverage is 0% and none of the four parameters has an enum constraint, so the description must compensate and does: text is the text to check, language accepts 'auto' (default, all six) or a comma-separated subset, mode is mask (default) or detect, and mask_char is a single character defaulting to '*'. All four parameters gain meaning that is absent from the schema.

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

Purpose5/5

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

States a specific verb and resource — finds and masks profanity — plus the exact supported languages (en, ru, uk, de, es, fr) and the class of obfuscation handled. No sibling among the 80+ tools does profanity filtering, so the agent can route to it unambiguously without opening the schema.

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?

It gives useful operating context — language auto-detects across all six unless set, mode defaults to mask, mask_char defaults to '*' — which implicitly tells the agent how to call it in the common case. But there is no explicit when-to-use versus alternatives (e.g., prompt_guard, image_moderate, classify) and no when-not guidance, so the usage framing stays implied.

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

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