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 describes the tool's core functionality and image requirements well, but lacks information about permissions, rate limits, processing time, error conditions, or what the output looks like (though no output schema exists).

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 with three sentences that each serve distinct purposes: stating the core functionality, specifying effect requirements, and providing use case context. Every sentence adds value 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?

For a tool with 4 parameters, no annotations, and no output schema, the description provides good functional context but lacks important behavioral details. It explains what the tool does and parameter requirements well, but doesn't cover error handling, performance characteristics, or output format, leaving gaps 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?

With 100% schema description coverage, the baseline is 3. The description adds value by explaining the emotional context ('emotionally expressive videos'), categorizing effects (dual-character vs. single-image), and providing use case context ('social media content and creative storytelling') beyond what the schema provides.

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 ('apply pre-defined animation effects', 'create emotionally expressive videos') and resources ('static images', 'portraits'), and distinguishes it from siblings by focusing on animation effects rather than status checks, generation, or other video operations.

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 context on when to use this tool ('perfect for social media content and creative storytelling') and specifies requirements for different effect types (dual-character vs. single-image effects). However, it doesn't explicitly state when NOT to use it or name specific alternatives among sibling 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 provided, the description carries full burden for behavioral disclosure. It states the tool checks status but reveals nothing about what statuses exist, whether it polls or returns immediately, error conditions, or response format. This leaves significant gaps for a status-checking operation.

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 wasted words. It's front-loaded with the core purpose and appropriately sized for a simple tool with one parameter.

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 status-checking tool with no annotations and no output schema, the description is insufficient. It doesn't explain what status information is returned, possible states (e.g., pending, completed, failed), or how to interpret results. The context demands more completeness than provided.

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 'task_id' well-documented in the schema as 'The task ID returned from generate_image'. The description adds no additional parameter semantics beyond what the schema already provides, meeting the baseline for high schema coverage.

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 ('check') and resource ('status of an image generation task'), making the purpose immediately understandable. It doesn't explicitly differentiate from sibling tools like 'check_video_status' or 'list_tasks', but the specificity to 'image generation task' provides reasonable 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. It doesn't mention prerequisites (e.g., requiring a task_id from generate_image), when-not scenarios, or relationships with sibling tools like list_tasks for broader status checking.

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 only states the basic action. It lacks behavioral details such as whether this is a read-only operation, what status values might be returned, if there are rate limits, or how errors are handled for invalid task IDs.

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 wasted words. It is appropriately sized for a simple tool and front-loads the essential information without unnecessary elaboration.

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 tool with no annotations and no output schema, the description is insufficient. It doesn't explain what status information is returned, possible states (e.g., pending, completed, failed), or error conditions. Given the complexity of task monitoring, more context is needed for effective 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?

Schema description coverage is 100%, so the input schema fully documents the 'task_id' parameter. The description adds no additional semantic context beyond implying the ID comes from generation tools, which is already suggested by the schema's description. Baseline 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 action ('check') and resource ('video generation task'), making the purpose understandable. However, it doesn't differentiate from the sibling tool 'check_image_status' beyond the resource type, missing explicit distinction between video and image status checks.

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 for monitoring video generation tasks, but provides no explicit guidance on when to use this tool versus alternatives like 'list_tasks' or 'check_image_status'. It mentions the task ID comes from specific generation tools, offering some contextual hint.

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. It discloses key behavioral traits: video constraints (clear face, 10s limit), supported character types (human/3D/2D, not animals), and TTS vs custom audio options. However, it doesn't cover permissions, rate limits, or what happens to the original video.

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 (4 sentences) and front-loaded with the core purpose. Every sentence adds value: purpose, input options, video requirements, and constraints. No wasted words or redundant 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?

For a 6-parameter creation tool with no annotations or output schema, the description provides good context about what the tool does and its constraints. It covers the main use case and limitations, though could benefit from more behavioral details about the creation process and output format.

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 adds some context about TTS/custom audio trade-offs and video requirements, but doesn't provide additional parameter semantics beyond what's in the schema descriptions.

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 ('create', 'synchronizing') and resources ('lip-sync video', 'mouth movements', 'audio'). It distinguishes from siblings by focusing on lip-sync creation rather than effects, generation, or status 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 provides clear context for when to use this tool: for creating lip-sync videos with specific input requirements (clear human face, video length ≤10s). It doesn't explicitly mention when not to use it or name alternatives among siblings, but the constraints guide appropriate 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?

With no annotations provided, the description carries full burden. It discloses key behavioral traits: it's a generative extension tool ('generating new content'), mentions the AI provider ('Kling AI'), and specifies the duration range ('4-5 seconds'). However, it doesn't cover important aspects like rate limits, authentication needs, cost implications, or what the output looks like (e.g., returns a new task ID).

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 (three sentences) and front-loaded with the core purpose. Every sentence adds value: first states the action, second explains the mechanism, third provides usage context. It could be slightly more concise by combining ideas, but there's minimal waste.

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 generative tool with 5 parameters, 100% schema coverage, but no annotations or output schema, the description is adequate but has gaps. It covers the what and why well, but lacks details on behavioral constraints (e.g., rate limits), output format, or error conditions. The context is complete enough for basic use but not for robust agent operation.

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%, providing full parameter documentation. The description adds minimal value beyond the schema, only implying that 'task_id' refers to 'a previous generation' and 'prompt' guides 'what should happen next'. It doesn't explain parameter interactions or provide additional context, so 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 clearly states the specific action ('Extend a video by 4-5 seconds using Kling AI') and resource ('a video'), distinguishing it from siblings like generate_video (create new) or apply_video_effect (modify existing). It explains the functional outcome ('continue a video beyond its original ending, generating new content that seamlessly follows from the last frame').

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 context for when to use this tool ('Perfect for creating longer sequences or adding additional scenes to existing videos'), but doesn't explicitly state when not to use it or name alternatives among siblings (e.g., generate_video for new videos, apply_video_effect for modifications). The guidance is helpful but lacks explicit 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 for behavioral disclosure. It mentions 'high-quality images' and 'creative control' but fails to disclose critical behavioral traits such as rate limits, authentication requirements, processing time, cost implications, or what happens on failure. For a complex 7-parameter tool with no 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 appropriately sized with three sentences that efficiently cover purpose, features, and capabilities. It's front-loaded with the core functionality and avoids unnecessary repetition. Every sentence adds value, though it could be slightly more structured with clearer separation of key features.

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 image generation tool with 7 parameters, no annotations, and no output schema, the description is incomplete. It doesn't explain what the tool returns (image URLs? base64? metadata?), doesn't mention error conditions, rate limits, or authentication requirements, and provides insufficient behavioral context for safe and effective 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?

Schema description coverage is 100%, so the schema already documents all parameters thoroughly. The description adds marginal value by mentioning 'multiple aspect ratios' and 'optional character reference support' which map to aspect_ratio and ref_image_url parameters, but doesn't provide additional semantic context beyond what's in the schema. 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 tool's purpose with specific verbs ('generate images from text prompts') and resources ('using Kling AI'), distinguishing it from sibling tools like generate_video or generate_image_to_video. It explicitly mentions the core functionality of text-to-image generation with quality and aspect ratio options.

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 for creative image generation with Kling AI, but provides no explicit guidance on when to use this tool versus alternatives like generate_image_to_video or apply_video_effect. It mentions 'creative control' as a general context but lacks specific when/when-not scenarios or comparisons to sibling 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?

No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool generates a video but lacks details on permissions, rate limits, processing time, output format, or error handling. For a complex video generation 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 to understand at a glance.

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 (8 parameters, video generation) and lack of annotations and output schema, the description is incomplete. It doesn't address behavioral aspects like processing time, output format, or error conditions, which are critical for an AI agent to use this tool effectively in context with 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?

Schema description coverage is 100%, meaning all parameters are documented in the input schema. The description adds no additional parameter information beyond what's in the schema, such as explaining relationships between parameters (e.g., how 'image_tail_url' interacts with 'prompt'). 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 from an image') and specifies the technology used ('using Kling AI'), which provides a specific verb+resource combination. However, it doesn't differentiate this tool from sibling tools like 'generate_video' or 'extend_video', which likely have different purposes or inputs.

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 'generate_video' or 'extend_video', nor does it specify prerequisites such as needing an image URL or appropriate prompts. Usage context is implied but not 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?

With no annotations provided, the description carries the full burden of behavioral disclosure. While 'Generate a video' implies a creation/mutation operation, the description doesn't address critical behavioral aspects: whether this is an async operation (likely given sibling 'check_video_status'), what permissions or authentication might be required, rate limits, cost implications, or what format/quality the output video will have. This is inadequate for a complex generative 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?

The description is a single, efficient sentence that states the core purpose without unnecessary elaboration. It's appropriately sized and front-loaded with the essential information, making it easy for an agent to quickly understand what the tool does.

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 8 parameters (including nested objects), no annotations, and no output schema, the description is insufficient. It doesn't address the asynchronous nature suggested by sibling tools, doesn't explain what the tool returns (video file? URL? task ID?), and provides no context about the Kling AI service's capabilities or limitations. The description should do more to compensate for the lack of structured metadata.

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 schema description coverage is 100%, so all parameters are documented in the schema itself. The description adds no additional parameter semantics beyond what's already in the schema descriptions. This meets the baseline expectation when schema coverage is complete, but doesn't provide extra value like explaining parameter interactions or practical usage patterns.

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 from text prompt using Kling AI' - a specific verb ('Generate') with resource ('video') and technology context ('Kling AI'). However, it doesn't distinguish this from sibling tools like 'generate_image_to_video' or 'extend_video', which would require explicit differentiation for 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 like 'generate_image_to_video', 'extend_video', or 'apply_video_effect'. There's no mention of prerequisites, appropriate contexts, or limitations that would help an agent choose between these video-related 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 provided, the description carries full burden for behavioral disclosure. It describes what information is returned but doesn't mention authentication requirements, rate limits, error conditions, or whether this is a read-only operation. The description is informative but lacks critical operational context.

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 concise with two sentences that efficiently convey the tool's purpose and scope. It's front-loaded with the main function and follows with additional detail about what's included in the overview.

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 tool with no output schema, the description provides adequate information about what the tool does but lacks details about the return format, error handling, or authentication requirements. Given the complexity is low (no parameters), the description is reasonably complete but could benefit from more operational 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 tool has 0 parameters with 100% schema description coverage, so the baseline is 4. The description appropriately doesn't discuss parameters since none exist, focusing instead on the tool's purpose and output.

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 ('check', 'provides') and resources ('account balance', 'total available credits', 'account status', 'breakdown by resource packages'). It distinguishes itself from siblings like 'get_resource_packages' by focusing on overall account status rather than just package details.

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 context (checking account status) but doesn't explicitly state when to use this tool versus alternatives like 'get_resource_packages' or other sibling tools. No guidance on prerequisites or exclusions is provided.

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 implies a read-only operation by using 'Get' and describes the type of information returned, but does not specify authentication requirements, rate limits, or potential errors. The description adds some context about usage monitoring but lacks detailed behavioral traits.

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 two sentences: the first states the purpose and details retrieved, and the second provides usage context. Every sentence adds value without waste, making it appropriately sized and front-loaded for clarity.

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 (simple read operation with no parameters) and lack of annotations or output schema, the description is adequate but has gaps. It explains what information is retrieved but does not describe the return format, potential errors, or authentication needs, leaving some contextual information incomplete for a tool without structured support.

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 0 parameters with 100% schema description coverage, so no parameter information is needed in the description. The description appropriately focuses on the tool's purpose and usage without redundant parameter details, earning a baseline score of 4 for this dimension.

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 ('Get detailed information') and resources ('Kling AI resource packages'), and distinguishes it from siblings by focusing on usage monitoring rather than content generation or task management. It explicitly mentions what information is retrieved: remaining credits, expiration dates, and package types.

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 context for when to use this tool ('Useful for monitoring API usage and planning resource allocation'), which helps differentiate it from siblings like get_account_balance or list_tasks. However, it does not explicitly state when not to use it or name specific alternatives, keeping it at a 4 rather than 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?

No annotations are provided, so the description carries the full burden. It discloses that the tool supports pagination for large task lists, which is a useful behavioral trait. However, it lacks details on permissions, rate limits, error handling, or the structure of returned data, leaving gaps in understanding the tool's full behavior.

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 concise and front-loaded, starting with the core purpose. Both sentences earn their place by adding context (filtering options and pagination support). It avoids unnecessary details, making it efficient, though it could be slightly more structured by explicitly separating purpose from features.

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 (5 parameters, no output schema, no annotations), the description is somewhat complete but has gaps. It covers the purpose and key features like filtering and pagination, but without annotations or output schema, it lacks details on authentication, error handling, and return format, which are important for full 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%, meaning all parameters are well-documented in the schema itself. The description mentions filtering by date range or status, which aligns with the schema but doesn't add significant meaning beyond it. With high schema coverage, the baseline score is 3, as the description provides minimal extra value for 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 tool's purpose: 'List all your Kling AI generation tasks with filtering options.' It specifies the resource (Kling AI generation tasks) and the action (list with filtering). However, it doesn't explicitly differentiate from sibling tools like 'check_image_status' or 'check_video_status', which might also involve task status checking, though those appear more specific to media types.

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 mentioning 'View task history, check statuses, and filter by date range or status,' suggesting it's for monitoring and filtering tasks. However, it doesn't provide explicit guidance on when to use this tool versus alternatives like the status-checking siblings (e.g., 'check_image_status'), nor does it specify any exclusions or prerequisites for use.

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. While it mentions the core functionality, it fails to disclose critical behavioral traits such as required image formats, processing time, rate limits, authentication needs, or what happens with invalid inputs. For a tool with no annotation coverage, this leaves significant gaps in understanding its operational behavior.

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 in the first sentence, followed by specific details in the second. Both sentences earn their place by clarifying the upload process and outfit capabilities without any wasted words, making it efficient 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?

Given the tool's moderate complexity (3 parameters, no output schema, and no annotations), the description is adequate for basic understanding but incomplete. It covers what the tool does but lacks details on behavioral aspects like error handling or output format, which are crucial for effective use without structured annotations or output schema.

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 adds minimal value beyond the schema by mentioning 'up to 5 clothing items' and 'complete outfit visualization,' which slightly reinforces the cloth_image_urls parameter's purpose but doesn't provide additional syntax or format details. 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 specific action ('Apply virtual clothing try-on'), resource ('to a person image using AI'), and scope ('upload a person image and up to 5 clothing items'). It distinguishes this tool from siblings like generate_image or apply_video_effect by focusing on clothing visualization rather than general image/video generation or effects.

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 for outfit visualization with single or multiple clothing items, but provides no explicit guidance on when to use this tool versus alternatives like generate_image for creating images from scratch. It mentions the capability but lacks context about prerequisites or exclusions.

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