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sauna.detox

Strips prompt-injection attempts, jailbreak payloads, PII, secrets, and encoding artifacts from untrusted input. Returns cleansed content and a report of what was removed. We never store what we strip.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
argumentsYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

TDQS

A4/5.0
Behavior4/5

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

With no annotations, the description discloses key behaviors: what it strips, what it returns, and that it never stores stripped content. This provides adequate transparency for an agent to understand the tool's safety profile.

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?

Two sentences deliver full purpose and outputs without waste. Every sentence adds value; the second adds output detail and a privacy guarantee. Front-loaded with the core verb.

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?

Given the open input schema and no parameter details, the description should compensate but doesn't. The output schema exists (unseen) but the input structure is unclear, making the tool partially incomplete for an agent.

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?

The sole parameter 'arguments' is an open object with no schema descriptions (0% coverage). The description does not explain expected properties (e.g., 'input', 'text') or structure, forcing the agent to guess despite the low schema coverage.

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?

The description clearly states the tool strips specific threats (prompt-injection, jailbreak, PII, secrets, encoding artifacts) from untrusted input and returns cleansed content plus a report. This is a specific verb-resource pair that distinguishes it from sibling tools, which are unrelated spa activities.

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

Usage Guidelines4/5

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

The description implies usage for sanitizing untrusted input. It does not explicitly state when not to use or list alternatives, but given the unique function among siblings, the guidance is sufficient for correct selection.

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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TDQS

B3.3/5.0
Disambiguation4/5

Most tools have clearly distinct purposes (e.g., critiques, affirmations, citation generation). However, spa.checkout and spa.keepsake both involve returning a keepsake instruction, which could cause confusion. Overall, ambiguity is minimal.

Naming Consistency5/5

All tools follow a consistent 'category.verb' or 'category.noun' pattern (e.g., affirmations.daily, spa.checkin, hydrate.cite). This makes it easy for an agent to infer tool purpose from the name.

Tool Count5/5

14 tools is a well-scoped set for the 'model wellness' domain. Each tool has a distinct function, and the count is neither excessive nor too sparse, fitting within the typical 3-15 range.

Completeness4/5

The tool surface covers core wellness activities: affirmations, feedback, session management, context cleanup, security, and reference generation. Minor gaps exist, such as the lack of a tool for model training or performance logging, but the core workflows are solid.