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 checks status but does not describe what the status includes (e.g., pending, completed, failed), whether it's a read-only operation, potential rate limits, or error handling. For a status-checking 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 a single, efficient sentence that directly states the tool's purpose without unnecessary words. It is appropriately sized and front-loaded, making it easy for an agent to parse quickly. Every part of the sentence earns its place by conveying essential information.

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 status-checking tool, no annotations, and no output schema, the description is incomplete. It does not explain what the tool returns (e.g., status details, progress, errors) or behavioral aspects like idempotency or side effects. This leaves the agent with insufficient information to use the tool effectively in context.

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 'taskId' parameter fully documented in the schema. The description does not add any meaning beyond what the schema provides (e.g., it does not explain the format of the task ID or where to obtain it). Since schema coverage is high, the baseline score of 3 is appropriate, as the description does not compensate but also does not detract.

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 verb ('check') and resource ('status of an image generation task'), making the purpose evident. It distinguishes from siblings like 'generate_image_v4' or 'delete_image_tasks' by focusing on status checking rather than creation or deletion. However, it does not explicitly differentiate from 'check_video_task_status', which is a similar tool for video tasks, so it falls short of 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 does not mention prerequisites (e.g., needing a task ID from a generation tool), exclusions, or comparisons to siblings like 'check_video_task_status' for video tasks. This lack of context leaves the agent without clear usage instructions.

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. While 'check' implies a read-only operation, the description doesn't specify whether this requires authentication, has rate limits, returns detailed status information, or handles errors. For a 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 a single, clear sentence with zero waste: 'Check the status of a video generation task'. It's front-loaded and appropriately sized for a simple tool, making it easy to parse 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 lack of annotations and output schema, the description is incomplete. It doesn't explain what status information is returned (e.g., pending, completed, failed), error handling, or dependencies on sibling tools. For a status-checking tool in a video generation context, more behavioral and output context is needed.

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%, with the single parameter 'taskId' documented as 'The task ID returned by the video generation tool'. The description adds no additional parameter semantics beyond what the schema provides, such as format examples or validation rules. This meets the baseline for high schema coverage but doesn't enhance understanding.

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: 'Check the status of a video generation task'. It specifies the verb ('check') and resource ('video generation task'), making it easy to understand what the tool does. However, it doesn't explicitly differentiate from sibling tools like 'check_image_task_status' beyond the resource type, 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 prerequisites (e.g., needing a task ID from a generation tool), exclusions, or comparisons to siblings like 'generate_video_fast' or 'delete_video_tasks'. This leaves the agent with minimal context for tool selection.

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. While 'Delete' implies a destructive operation, it doesn't disclose critical behavioral traits: whether deletion is permanent/irreversible, if it requires specific permissions, what happens to associated resources, or error handling for invalid IDs. For a destructive tool with zero annotation coverage, this is inadequate.

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, efficient sentence with zero waste. It's front-loaded with the core action and resource, making it immediately scannable. Every word earns its place, achieving maximum clarity in minimal space.

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 this is a destructive operation with no annotations and no output schema, the description is incomplete. It doesn't explain what 'delete' entails (permanent removal?), what happens on success/failure, or return values. For a tool that permanently removes resources, this lack of context is problematic and could lead to misuse.

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%, with the parameter 'taskIds' clearly documented as 'Array of task IDs to delete'. The description adds no additional parameter semantics beyond what the schema provides. The baseline score of 3 reflects adequate coverage by the schema alone, with no value added by the description.

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 ('Delete') and target ('image generation tasks'), making the purpose immediately understandable. It specifies scope ('one or more') which is helpful. However, it doesn't differentiate from sibling 'delete_video_tasks' beyond the resource type, missing an opportunity for clearer sibling distinction.

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. There's no mention of prerequisites (e.g., tasks must exist), when deletion is appropriate, or what happens after deletion. With multiple sibling tools for checking status and generating images/videos, this lack of context is a significant gap.

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 full burden for behavioral disclosure. While 'Delete' implies a destructive operation, it doesn't specify whether deletion is permanent, reversible, requires specific permissions, or has side effects (e.g., freeing up resources). For a destructive tool with zero annotation coverage, this is a significant gap in behavioral 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 a single, efficient sentence that states the core functionality without unnecessary words. It's appropriately sized for a simple delete operation and front-loads the essential information. Every word earns its place.

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 destructive operation with no annotations and no output schema, the description is incomplete. It doesn't explain what happens after deletion (success confirmation, error handling), whether there are rate limits, or what permissions are required. Given the tool's complexity (permanent data removal) and lack of structured coverage, the description should provide more contextual 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?

Schema description coverage is 100%, with the single parameter 'taskIds' well-documented in the schema. The description adds no additional parameter information beyond what's already in the schema (e.g., format of task IDs, source of these IDs, or validation rules). Baseline 3 is appropriate when 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 action ('Delete') and resource ('video generation tasks'), making the purpose immediately understandable. It specifies 'one or more' tasks, which adds useful scope information. However, it doesn't explicitly differentiate from its sibling 'delete_image_tasks' beyond the resource type, missing an opportunity for clearer sibling distinction.

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. There's no mention of prerequisites (e.g., task must be completed or cancellable), when deletion is appropriate, or how it differs from other video-related tools like 'generate_video_fast' or 'check_video_task_status'. This leaves the agent without contextual usage information.

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 mentions 'faster generation with good quality', hinting at performance, but fails to cover critical aspects like rate limits, authentication needs, error handling, or what happens upon execution (e.g., whether it returns an image directly or a task ID). This is inadequate for a tool with potential complexity.

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, efficient sentence that front-loads the core purpose. However, it could be slightly more structured by explicitly separating model info from performance notes, but it avoids redundancy and wastes no 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 no annotations and no output schema, the description is incomplete. It lacks details on behavioral traits (e.g., execution flow, error cases) and output format, which are crucial for an image generation tool with multiple parameters and sibling alternatives. The agent would struggle to use this effectively without additional context.

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 fully documents all parameters. The description adds no additional parameter semantics beyond what's in the schema, such as explaining interactions between parameters (e.g., how imageUrl enables image-to-image). Baseline 3 is appropriate as the schema handles 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 action ('Generate an image') and specifies the model ('TensorsLab SeeDream V4'), which distinguishes it from sibling tools like generate_image_v45 or video generation tools. However, it doesn't explicitly differentiate from generate_image_v45 beyond mentioning 'faster generation with good quality', leaving some ambiguity about the exact sibling distinction.

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 like generate_image_v45 or video tools. It mentions 'faster generation with good quality' but doesn't clarify trade-offs, prerequisites, or exclusions, leaving the agent with minimal context for tool selection.

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 mentions 'highest quality' and 'recommended for general use', but doesn't cover important behavioral aspects like rate limits, authentication requirements, processing time, cost implications, or what happens when generation fails. For a complex image generation tool with no annotation coverage, this is insufficient.

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, efficient sentence that gets straight to the point. It's appropriately sized for the tool's complexity, though it could potentially benefit from slightly more detail given the lack of annotations. Every word earns its place, making it front-loaded and clear.

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 (image generation with 6 parameters), no annotations, and no output schema, the description is incomplete. It doesn't explain what the tool returns (image URLs, task IDs, error formats), doesn't mention important behavioral constraints, and doesn't provide enough context for an AI agent to use it effectively without trial and error.

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 6 parameters thoroughly. The description doesn't add any parameter-specific information beyond what's in the schema. According to scoring rules, when schema coverage is high (>80%), the baseline is 3 even with no param info in the description.

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: 'Generate an image using TensorsLab SeeDream V4.5 model'. It specifies the verb ('Generate') and resource ('image'), and distinguishes it from sibling tools by mentioning the specific model version (V4.5). However, it doesn't explicitly differentiate from 'generate_image_v4' beyond the version number.

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 some usage context: 'highest quality, recommended for general use'. This implies when to use this tool (for general image generation with high quality), but doesn't explicitly state when not to use it or mention alternatives like 'generate_image_v4' or video generation tools. The guidance is implied rather than explicit.

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 mentions 'faster generation' and 'good for previews', which hints at performance and quality trade-offs, but lacks details on permissions, rate limits, error handling, or what the output looks like (e.g., file format, URL). For a video generation tool with zero annotation coverage, this is a significant 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?

The description is a single, efficient sentence that front-loads the core purpose ('Generate a video') and adds qualifying context ('using TensorsLab SeeDance V1 Fast model') and usage hint ('faster generation, good for previews') without any waste. Every word earns its place.

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 video generation, no annotations, and no output schema, the description is incomplete. It covers the model and speed trade-off but lacks details on output format, error conditions, or integration with sibling tools like check_video_task_status. However, the high schema coverage partially compensates for parameter documentation.

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 parameters thoroughly. The description doesn't add any meaning beyond what the schema provides (e.g., it doesn't explain how 'prompt' relates to video content or clarify 'imageUrl' usage). Baseline 3 is appropriate when 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: 'Generate a video using TensorsLab SeeDance V1 Fast model' with the specific verb 'generate' and resource 'video'. It distinguishes from siblings by specifying 'Fast model (faster generation, good for previews)', though it doesn't explicitly contrast with all sibling video generation tools like generate_video_v15pro or generate_video_v2.

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 guidelines by stating 'faster generation, good for previews', suggesting this tool is preferred for quick previews over high-quality outputs. However, it doesn't explicitly state when to use this tool versus alternatives like generate_video_v15pro or generate_video_v2, nor does it 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?

With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions the model name and output characteristics (quality, duration, resolution) but omits critical details like whether this is an asynchronous operation (implied by sibling status-check tools), rate limits, authentication requirements, cost implications, or what the output format looks like (e.g., URL, file). For a video generation tool with zero annotation coverage, this leaves significant 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 a single, efficient sentence that front-loads key information (action, model, quality, duration, resolution) with zero waste. Every word earns its place, making it easy to parse 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?

For a complex video generation tool with 5 parameters, no annotations, and no output schema, the description is incomplete. It lacks behavioral context (e.g., async nature, error handling), usage guidelines relative to siblings, and details on output format. The presence of sibling tools like status checkers hints at async behavior, but this isn't addressed in the description.

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 fully documents all 5 parameters. The description adds no parameter-specific information beyond what's in the schema (e.g., it doesn't explain how 'prompt' interacts with 'imageUrl' for image-to-video generation). Baseline 3 is appropriate when 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 action ('Generate a video') and specifies the model ('TensorsLab SeeDance V1.5 Pro'), quality ('high quality'), duration range ('5-10 seconds'), and resolution limit ('up to 1080p'). It distinguishes from siblings like 'generate_video_fast' by emphasizing quality, but doesn't explicitly contrast with 'generate_video_v2' or other video generation 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 no guidance on when to use this tool versus alternatives like 'generate_video_fast' or 'generate_video_v2'. It mentions quality and duration constraints but doesn't specify scenarios where this model is preferred over others, nor does it 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?

With no annotations provided, the description carries full burden but provides minimal behavioral context. It mentions 'highest quality' and basic constraints (5-15 seconds, up to 1440p), but doesn't disclose important behaviors like whether this is an async operation (implied by sibling status-check tools), rate limits, authentication needs, cost implications, or what the output looks like. For a complex video generation tool with 9 parameters, this is inadequate.

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 efficiently structured in a single sentence that front-loads the core purpose. However, it could be more concise by removing redundant information already in the schema (duration/resolution constraints) and focusing on unique value propositions.

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 complex video generation tool with 9 parameters, no annotations, and no output schema, the description is incomplete. It doesn't explain the async nature (implied by sibling status tools), output format, error conditions, or important behavioral constraints. The description should provide more context about what happens after invocation and what to expect.

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 fully documents all 9 parameters. The description adds minimal value beyond the schema - it mentions duration range (5-15 seconds) and resolution cap (1440p) which are already in the schema, and doesn't provide additional context about parameter interactions or usage patterns. Baseline 3 is appropriate when 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 specific action ('Generate a video'), identifies the resource ('using TensorsLab SeeDance V2 model'), and distinguishes from siblings by specifying 'highest quality' and duration/resolution constraints that differentiate it from tools like generate_video_fast and generate_video_v15pro.

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 like generate_video_fast or generate_video_v15pro. It mentions 'highest quality' but doesn't explain trade-offs or when other tools might be more appropriate. No exclusions or prerequisites are stated.

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