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

With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively describes key behaviors: the tool performs AI-based image editing/combination, supports multiple image inputs, handles both URLs and Base64 data (implied from schema), and returns a Task ID, trace ID, and image URL. It also mentions asynchronous processing via callback_url. However, it lacks details on rate limits, error conditions, or specific permissions required.

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 with clear sections (purpose, usage guidelines, common use cases, returns) and front-loaded key information. However, it includes some redundancy (e.g., 'Common use cases' partially overlaps with 'Use this when:') and could be more concise by merging similar points, though all sentences contribute meaningful context.

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 complexity (AI-based image editing with multiple inputs), no annotations, and an output schema (implied by 'Returns' section), the description is highly complete. It covers purpose, usage scenarios, behavioral aspects, and return values, providing sufficient context for an agent to understand when and how to invoke the tool effectively without relying on structured fields.

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%, so the schema already documents all three parameters thoroughly. The description adds minimal value beyond the schema by briefly mentioning 'text prompt' and 'multiple images' in the opening, but does not provide additional syntax, format, or usage details for parameters. The baseline score of 3 is appropriate as the schema does the heavy lifting.

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 verbs ('edit or combine images using AI based on a text prompt') and distinguishes it from sibling tools like 'nanobanana_generate_image' by focusing on modifying existing images rather than generating new ones from scratch. It explicitly mentions the resource ('images') and the AI-driven mechanism.

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 explicit guidance on when to use this tool through a dedicated 'Use this when:' section listing five specific scenarios (e.g., combining multiple images, modifying an image, virtual try-on). It also distinguishes from alternatives by implying this is for editing existing images, unlike 'nanobanana_generate_image' for generation from scratch.

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 full burden of behavioral disclosure. It effectively describes key behaviors: it's a generative operation ('creates high-quality images'), mentions quality dependencies ('more descriptive your prompt, the better the results'), and specifies the return format ('Task ID, trace ID, and generated image URL'). However, it doesn't cover potential limitations like rate limits, error conditions, or authentication needs, leaving some gaps.

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 front-loaded, starting with the core purpose, followed by usage guidelines, tips, and return details. Each sentence earns its place by adding actionable information (e.g., prompt examples, when-to-use criteria) without redundancy. It's appropriately sized for a tool with two parameters and clear functionality.

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 complexity (generative AI image creation), the description is complete enough. It covers purpose, usage, behavioral aspects, and return values, and with an output schema present, it doesn't need to explain return values in detail. The combination of description, schema (100% coverage), and output schema provides a comprehensive context for the 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?

Schema description coverage is 100%, so the schema already documents both parameters thoroughly. The description adds value by reinforcing the prompt's importance ('The more descriptive your prompt, the better the results') and providing detailed prompt writing tips with an example, which enhances understanding beyond the schema's technical descriptions. It doesn't add new parameter details but improves contextual usage.

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 specific action ('Generate an AI image from a text prompt') and resource ('using Google's Nano Banana model'), distinguishing it from sibling tools like nanobanana_edit_image (which edits existing images) and nanobanana_get_task (which retrieves task status). It explicitly mentions creating high-quality images from detailed descriptions, establishing its unique purpose.

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 explicit usage scenarios with a bulleted list ('Use this when:') that includes when to use this tool (e.g., 'generate a new image from scratch') and implicitly when not to use it (e.g., for editing existing images, which is handled by nanobanana_edit_image). It also offers prompt writing tips, further guiding effective usage.

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. It discloses that the tool queries task status and retrieves results, which implies it's a read-only operation without side effects. However, it lacks details on potential errors (e.g., invalid task IDs), rate limits, or authentication needs, leaving behavioral gaps for an agent.

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 front-loaded, starting with a clear purpose statement followed by usage guidelines and return details. Each sentence adds essential information without redundancy, making it efficient and easy to parse.

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 moderate complexity (single parameter, read-only query), the description is mostly complete. It covers purpose, usage, and returns, and an output schema exists, so return values need not be detailed. However, it lacks error handling or behavioral constraints, slightly reducing completeness.

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 input schema has 100% description coverage, so the baseline is 3. The description adds value by explaining the parameter's origin ('task_id' from generation/edit responses) and its role in retrieving task details, enhancing understanding beyond the schema's technical definition.

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 verbs ('Query the status and result') and resources ('image generation or edit task'), distinguishing it from siblings like nanobanana_generate_image (which creates tasks) and nanobanana_get_tasks_batch (which handles multiple tasks). It explicitly mentions retrieving image URLs and metadata, which sets it apart from creation tools.

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 explicit usage scenarios in a bulleted list, including when to use it (e.g., to check completion status or retrieve image URLs) and implies when not to use it (e.g., not for creating new tasks, which is handled by nanobanana_generate_image or nanobanana_edit_image). This offers clear guidance on context and alternatives.

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 describes the tool as a query/check operation, implying it's read-only and non-destructive, and mentions efficiency benefits. However, it lacks details on potential rate limits, error handling, or authentication needs. The description adds some context but doesn't fully cover behavioral traits beyond the basic purpose.

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 front-loaded with the core purpose, followed by efficiency comparison, usage guidelines, and return information. Each sentence adds value without redundancy, and the structure is clear and well-organized. It's appropriately sized for the tool's complexity, with no wasted words.

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 (single parameter, no nested objects), high schema coverage, and the presence of an output schema (which handles return values), the description is complete enough. It covers purpose, usage guidelines, efficiency context, and return summary, addressing all necessary aspects without needing to duplicate schema information.

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 input schema has 100% description coverage, with the parameter 'task_ids' well-documented as 'List of task IDs to query. Allows querying multiple tasks at once.' The description doesn't add any additional meaning beyond this, such as format examples or constraints. With high schema coverage, the baseline score of 3 is appropriate as the schema handles the parameter documentation adequately.

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 as 'Query multiple image generation/edit tasks at once' and 'Efficiently check the status of multiple tasks in a single request,' specifying the verb (query/check), resource (tasks), and scope (multiple/batch). It explicitly distinguishes from the sibling tool nanobanana_get_task by noting it's 'More efficient than calling nanobanana_get_task multiple times,' providing clear differentiation.

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 includes explicit usage guidelines with a 'Use this when:' section listing three specific scenarios (e.g., 'You have multiple pending generations to check'), and it names the alternative tool nanobanana_get_task, providing clear when-to-use and when-not-to-use guidance. This directly helps the agent choose between this tool and its sibling.

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