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Lovora

Get Lovora content rules

get_content_rules
Read-only

What content Lovora allows and prohibits (adults-only; explicit content between fictional adults allowed; prohibited categories), with the link to the full Acceptable Use Policy.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.5/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true, and the description adds concrete behavioral context by specifying the content categories (adults-only, explicit content between fictional adults allowed, prohibited categories) and the presence of a link to the full policy. This goes beyond a simple read-only claim, giving the agent a preview of what the response will contain without contradiction.

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, tightly packed sentence that front-loads the core purpose (what content rules are) and appends the link detail. There is zero filler, and every phrase contributes meaning.

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?

For a simple zero-parameter getter with no output schema, the description fully conveys what the tool returns (a summary of allowed/prohibited content and a link to the policy). An agent can call it without needing any additional context. The read-only annotation and the explicit content categories make it complete.

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

Parameters4/5

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

The tool has zero parameters and schema coverage is 100% (vacuously). Per the rubric, a 0-parameter tool gets a baseline of 4. The description adds no parameter information because none exists, but it does clarify what the returned data is, which is sufficient.

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 states a clear verb ('get') and resource ('content rules'), and explicitly enumerates what the tool returns (allowed/prohibited content categories and a link to the policy). It is easily distinguishable from siblings like get_pricing and list_companions because it names the specific policy topic.

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 when to use the tool: whenever an agent needs to know Lovora's content policy before acting. It does not explicitly list exclusions or alternatives, but given the sibling set (pricing, companions, docs), the purpose is obvious enough that no further routing is needed. A slightly more explicit 'use this to check policy before generating content' would push it to 5.

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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