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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions the download action and default behavior for the version parameter, but fails to describe critical aspects such as authentication requirements, rate limits, file formats, error handling, or what happens if the app/version doesn't exist. This leaves significant gaps for a tool that performs downloads.

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 well-structured and concise, with a clear opening sentence followed by bullet points for Args and Returns. Every sentence earns its place by providing essential information without redundancy, making it easy to scan and understand quickly.

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 complexity of a download operation with no annotations and no output schema, the description is incomplete. It lacks details on behavioral traits (e.g., authentication, errors), doesn't fully explain parameters, and the return value description ('Success message with download details') is vague without an output schema. This makes it inadequate for safe and effective use by an AI agent.

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 description adds basic semantics by explaining that 'app' can be a name or numeric ID and 'version' is optional with a default to latest, which provides context beyond the schema's 0% coverage. However, it doesn't fully compensate for the low coverage—for example, it doesn't clarify the format or constraints for 'output_dir' or provide examples, leaving some parameters inadequately explained.

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 ('download') and resource ('a specific version of an app'), with the specific verb 'download' and resource 'app' making the purpose unambiguous. However, it doesn't explicitly differentiate from sibling tools like 'get_app_latest_version' or 'search', which prevents a perfect score.

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 implied usage guidance by mentioning that 'If no version is specified, downloads the latest,' which suggests when to use the version parameter. However, it lacks explicit guidance on when to use this tool versus alternatives like 'get_app_latest_version' or 'search', and doesn't mention prerequisites or 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?

No annotations are provided, so the description carries full burden. It mentions the tool 'Get[s]' information and returns a 'Dictionary containing release information', but does not disclose behavioral traits such as whether it requires authentication, has rate limits, or how it handles errors. The description is minimal and lacks essential operational details.

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 appropriately sized and front-loaded, starting with the core purpose followed by structured 'Args' and 'Returns' sections. Every sentence earns its place by providing essential information without redundancy or fluff, making it easy to scan and understand.

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 no annotations, no output schema, and 3 parameters with 0% schema coverage, the description is incomplete. It covers the basic purpose and parameters but lacks details on return values (only mentions 'Dictionary' vaguely), error handling, or prerequisites. For a tool with this complexity, it should provide more context to be fully helpful.

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 0%, so the description must compensate. It adds meaning by explaining each parameter's purpose: 'app' as 'name or numeric ID', 'splunk_version' for 'compatibility with', and 'is_cloud' for 'check compatibility with Splunk Cloud'. This clarifies semantics beyond the bare schema, though it could provide more detail on formats or constraints.

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's purpose with specific verb ('Get') and resource ('latest compatible version of an app'), and distinguishes it from sibling tools like 'download_app' (which downloads) and 'search' (which searches). It specifies the context of 'for a specific Splunk version' and compatibility checking.

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 by stating 'for a specific Splunk version' and 'check compatibility with Splunk Cloud', but does not explicitly say when to use this tool versus alternatives like 'download_app' or 'search'. It provides context but lacks explicit guidance on exclusions or comparisons.

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 of behavioral disclosure. It states the tool searches and returns a list of results, but it doesn't cover important traits like whether it's read-only, if it has rate limits, authentication needs, pagination behavior, or error handling. For a search tool with zero annotation coverage, this is a significant gap in transparency.

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 appropriately sized and front-loaded, with a clear purpose statement followed by brief sections for args and returns. Every sentence earns its place by providing essential information without redundancy. However, minor improvements in structure (e.g., bullet points) could enhance readability.

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 low complexity (one parameter, no output schema, no annotations), the description is minimally adequate. It covers the basic purpose and parameter semantics but lacks details on behavioral traits, usage guidelines, and output specifics (e.g., result format). Without annotations or an output schema, more completeness would be beneficial for an AI agent.

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 description adds meaningful context for the single parameter 'query' by explaining it's 'The search query to search Splunkbase for,' which clarifies its purpose beyond the schema's basic title and type. Since schema description coverage is 0% and there's only one parameter, this compensation is effective, though it could be more detailed (e.g., query syntax).

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's purpose: 'Search Splunkbase for apps.' It specifies the verb ('search') and resource ('Splunkbase for apps'), making it easy to understand what the tool does. However, it doesn't explicitly differentiate from sibling tools like 'download_app' or 'get_app_latest_version', which prevents a perfect score.

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 guidance on when to use this tool versus alternatives. It doesn't mention sibling tools like 'download_app' or 'get_app_latest_version', nor does it specify any prerequisites, contexts, or exclusions for usage. This lack of comparative or contextual advice limits its helpfulness for an AI agent.

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