stageum

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

With no annotations, the description carries the burden of disclosure. It clearly states the data is self-reported and based on company documentation, not independent analysis. This is a valuable behavioral trait for the agent to know, though it stops short of describing return format or other potential edge cases.

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 two sentences, with the first sentence front-loading the action and resource, and the second adding crucial context about data provenance. Every word earns its place, with 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 simple no-parameter tool, the description is largely complete. It conveys the purpose and the key limitation (self-reported). It could go further by hinting at the return format (e.g., a list or prose), but the sibling set and title make the output type reasonably inferable.

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, so there are no parameter semantics to explain. The description correctly omits parameter details, and the baseline for no-parameter tools is 4.

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 uses a specific verb ('Returns') and identifies a clear resource ('Stageum's documented positioning relative to VR headset-based public speaking apps'). This distinguishes it from sibling tools like get_quick_start or list_plans, which target different content areas.

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 comparison data is needed) but does not explicitly state alternatives or exclusions. It mentions the self-reported nature, which is a contextual caveat, but lacks direct guidance on choosing this over other tools.

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?

With no annotations, the description carries the behavioral burden. It discloses that the tool returns instructions and that no account or credit card is needed, which is a key behavioral trait. It doesn't mention potential caveats, but for a simple no-parameter tool, this is adequate.

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 two sentences: the first states the action, the second adds context and benefit. Every word earns its place, with no redundancy or fluff.

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 parameterless tool with no output schema, the description fully covers its purpose, usage context, and key behavior (free, no signup). It is complete for the tool's simplicity and clearly distinguished from siblings.

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, so the schema carries no parameter semantics. Per the rubric, 0 parameters gets a baseline of 4. The description adds no parameter details, but none are needed.

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 states the tool returns step-by-step instructions for trying Stageum for free, using a specific verb ('returns') and resource. It distinguishes itself from sibling tools like list_plans or get_comparison by focusing on immediate free trial instructions.

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 this tool: when a user wants to test the platform immediately without account or credit card. It provides clear context but does not explicitly mention when not to use it or name alternative tools, so it stops short of a 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?

With no annotations provided, the description carries the burden of disclosing behavior. It explicitly states the return fields (names, descriptions, premium status, availability), giving an agent a clear expectation. The verb 'List' implies a read-only operation, adding implicit safety transparency beyond what structured data alone provides.

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 two sentences, front-loaded with the verb and resource, and contains no extraneous content. Every phrase earns its place, making it highly 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 list tool with no output schema, the description covers the essential return values and context. It states exactly what is listed and what fields are returned, making it complete for its intended use.

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 takes zero parameters and the schema is empty. The description does not need to elaborate on parameter semantics; per rubric, the baseline for 0 params is 4, and no additional meaning is required.

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 states a specific action ('List') on a specific resource ('3D virtual environments for public speaking practice') and enumerates the returned fields (names, descriptions, premium status, availability). This distinguishes it from sibling list tools like list_faqs or list_plans by domain and scope.

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 usage when a user needs available environments for public speaking practice, which is clear from the domain. It does not explicitly contrast with alternatives or state when not to use it, so it lacks exclusionary guidance but provides clear context.

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?

No annotations are provided, so the description carries the full burden. The verb 'List' signals a read operation, but the description adds no further behavioral details (e.g., whether it returns a simple list, caching behavior, or any rate limits). It is minimally adequate for a no-parameter list tool.

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, front-loaded sentence efficiently conveys the purpose without wasted words. Every part contributes to understanding the tool's scope.

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?

With no parameters, output schema, or annotations, the description covers the essential scope and topics. However, it does not explicitly describe the return format or usage boundaries, which could be clearer for an agent choosing between FAQ and other informational tools.

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, so there is no parameter documentation burden. The description's mention of covered topics adds semantic context about the output contents, which is helpful.

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 uses a specific verb ('List') and clearly identifies the resource (frequently asked questions) and the domain (Stageum). It further specifies the topics covered, distinguishing it from sibling tools like list_environments or list_plans.

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 the use case for answering common questions about Stageum by enumerating the covered topics (hardware, AI privacy, etc.). However, it does not explicitly state when to use this tool instead of siblings like get_comparison or list_use_cases, nor does it mention exclusions.

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?

With no annotations, the description carries the behavioral burden. It implicitly indicates a read-only list operation and discloses the return values (Free, Pro, Team tiers). However, it does not mention potential requirements (e.g., authentication) or other behavioral traits like caching or pagination. For a simple list, 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 well-structured sentence that front-loads the verb and resource. It avoids redundancy and includes only essential information about the output. Every word earns its place, and it is appropriately sized for a parameterless list tool.

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?

Given the tool's simplicity (no parameters, no output schema), the description is complete enough: it states what is listed and the specific tiers returned. It could slightly improve by mentioning if there are any limits or if it represents all plans, but for an informational list, this is sufficient.

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, so the baseline is 4. The description naturally has no parameter details to add, and the schema is fully compliant. The description adds context about what the returned data represents, which is valuable despite the absence of parameters.

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 uses the specific verb 'List' with a clear resource ('available subscription plans') and specifies the content (pricing, features, billing intervals). It also names the returned tiers (Free, Pro, Team), making its purpose unmistakable and distinct from sibling tools like list_environments or get_comparison.

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?

No guidance is provided on when to use this tool versus alternatives. It does not mention exclusions or scenarios, such as 'use get_comparison for side-by-side comparisons' or 'use list_environments for deployment targets.' The context signals show sibling tools, but the description does not help the agent choose among them.

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?

With no annotations, the description carries the full burden. It discloses the content (audience segments and use cases) and adds the specific audience profiles, but it does not explicitly state behavioral traits such as read-only nature, response format, or any side effects. The verb 'List' implies a safe read operation, which somewhat compensates.

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 two concise sentences, front-loaded with the primary action and followed by the target audience. Every word adds value, and it is well-structured for quick comprehension.

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?

Given that this is a simple, zero-parameter, no-output-schema tool, the description adequately covers what the tool returns and why it exists. It could mention possible output format but is complete enough for an agent to select and invoke it 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?

The tool has zero parameters, and the schema is trivially complete (100% coverage). The baseline for 0 parameters is 4, and the description does not need to explain parameters.

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 states the action ('List') and the resource ('audience segments and use cases'), and it distinguishes this tool from siblings by focusing on user profiles and platform fit. It includes a concrete list of audience types, making the purpose unambiguous.

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 clear contextual guidance: use this when determining whether Stageum fits a specific user profile (executives, remote teams, etc.). It does not explicitly mention alternatives or exclusions, but the use case is easily inferred from the stated purpose.

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