Lovora

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.

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.

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.

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.

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.

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.

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

Annotations already declare readOnlyHint=true, so the main safety-relevant behavior is covered. The description adds useful context such as 'Current', the launch-discount condition, and the list of included data, but it does not disclose potential caching, staleness, or authentication requirements. With annotations in place, this is adequate but not rich.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single front-loaded sentence that begins with the core purpose and then lists all relevant data categories without any filler. Every clause carries meaning, so it is concise and well-structured.

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

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

For a zero-parameter, read-only pricing tool with no output schema, the description covers all major content areas an agent would expect: plans, prices, discount status, credits, feature costs, top-up packs, free-tier limits, and payment methods. This is complete enough for correct invocation and interpretation.

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

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

The tool has zero parameters and the input schema is an empty object with 100% coverage, so there is no param ambiguity. The description adds no parameter-specific meaning because none is needed; baseline 4 applies.

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

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

The description clearly identifies the tool as a pricing lookup by opening with 'Current plans and prices in USD' and then enumerating exactly what is covered: credits, feature costs, top-up packs, free-tier limits, and payment methods. This distinguishes it from siblings like get_content_rules, list_companions, and search_docs, which are unrelated to pricing.

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

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

The intended use is clear from the description: an agent should call this tool when it needs current Lovora plan and pricing information. It does not explicitly mention when not to use it or name alternatives, but the sibling tools are clearly unrelated, so there is little ambiguity.

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

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

Annotations already show readOnlyHint=true, and the description adds useful behavioral context: results come from the 'published catalogue' (closed set, consistent with openWorldHint=false) and all entries are fictional adults. It does not disclose pagination/ordering, but with annotations covering the safety profile and the description covering the return fields, this is not a serious gap.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

A single sentence that is front-loaded with the core purpose and then lists the return fields, with no wasted words or duplication of schema content.

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

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

For a simple read-only list tool with only optional parameters, the description explains exactly what data is returned, and the schema covers parameter details. With no output schema, this description is enough for an agent to call the tool correctly.

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

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

Schema description coverage is 100%: both limit and style are fully documented in the schema, so the description does not need to repeat them. The description adds no parameter-specific meaning, so the baseline of 3 is appropriate.

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

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

The description names a specific action (list) and resource (the published catalogue of Lovora AI companions) and enumerates the returned fields (name, age, style, personality, short description, and chat URL). This clearly differentiates it from the sibling tools, which all concern other domains.

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

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

The description gives clear context: it is the catalogue-listing tool, so an agent would use it when the task is to retrieve companion profiles rather than pricing, docs, or content rules. It does not explicitly name alternatives or give exclusion criteria, but sibling domains are distinct enough that usage is easy to infer.

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

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

Annotations already declare readOnlyHint=true, so the safety profile is covered. The description adds value by disclosing that it returns matching documents with summaries and URLs. It does not mention result limits, ranking, or filtering behavior, but given the read-only annotation, this is an acceptable level of disclosure.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

A single, dense sentence that front-loads the action and then lists relevant content domains. The topic list is long but purposeful, giving the agent an immediate sense of scope. There is no redundancy or filler.

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

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

For a one-parameter, read-only search tool, the description covers purpose, scope, and return format. Since there is no output schema, the description tells the agent what to expect (documents, summaries, URLs). It does not detail the response structure or error behavior, but these are minor gaps for a simple tool.

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

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

Schema coverage is 100%, and the query parameter description already provides examples and intent. The description itself does not add additional parameter semantics beyond listing topic areas, which is context but not strictly parameter-level guidance. Baseline 3 is appropriate.

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

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

The description states a specific verb ('Search') and resource ('Lovora's reference documents'), enumerates content areas (pricing, free access, payments without a card, etc.), and specifies the return type ('summaries and URLs'). This clearly distinguishes it from sibling getters like get_pricing and get_content_rules, which target specific, narrower slices.

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

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

The description provides no explicit guidance on when to use this tool versus the siblings. It lists pricing among the topics, yet get_pricing exists, creating potential overlap without any routing instruction such as 'for exact pricing use get_pricing'. The only usage hint (query examples) appears in the schema, not in the description.

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