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

No annotations are provided, so the description carries the full behavioral burden. It usefully discloses the asynchronous, task-based nature of the call ('Returns: Task ID ... and state'), which is real context beyond the schema. But it omits permissions/auth requirements, latency expectations, whether generation is billed or rate-limited, and how the task should be polled afterwards.

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?

Two short sentences plus a brief returns note, front-loaded with the core action. Slightly bulky with a header-style 'Returns:' block and a trailing blank line, but nothing is wasted or repeated.

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?

An output schema exists, so return values need not be spelled out, and the alternative tool is named. But for a 14-parameter async generation tool with zero annotations, the description leaves the agent without model-selection guidance, async/polling workflow, or any note about which parameters only apply to certain models (e.g., media and duration=-1).

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 every parameter is already documented in the schema, including model enum values, resolution, duration ranges, and media types. The description adds no parameter syntax: it names wan2.6-t2v but never explains which of the five models to choose or what the media inputs require. Baseline 3 applies 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?

States a specific verb and resource ('Generate AI video from a text prompt') and names the underlying model (wan2.6-t2v), so the agent knows exactly what output to expect. It also explicitly distinguishes itself from the sibling wan_generate_video_from_image, so the tool is identifiable without opening the schema.

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?

Gives a clear routing rule: use this for text-to-video, use wan_generate_video_from_image for image-to-video. That is a genuine when/when-not pair. It does not, however, address the other siblings (wan_generate_video_all_in_one) or explain when to pick among the five selectable models, so guidance is clear but incomplete.

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 behavioral burden, and it says almost nothing beyond 'generate'. It omits whether the call is synchronous or returns a task for polling (callback_url and wan_get_task strongly suggest async), and says nothing about cost, rate limits, or failure 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?

A single front-loaded sentence with no filler; it is efficient and easy to scan. It is arguably too terse for a 9-parameter generator, but there is no wasted text.

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 9-parameter, unannotated generator with only 56% schema coverage, one sentence is insufficient. An output schema exists so return values need not be explained, but async behavior, parameter roles, and sibling differentiation are all missing.

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 coverage is only 56%, and the description adds zero parameter meaning beyond the schema. Key parameters such as seed, audio, watermark, and callback_url are undocumented in both places, and the description does not compensate for the coverage gap.

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?

States a specific verb+resource (generate a Wan 3 video) and enumerates the input modes it accepts, which hints at its 'all-in-one' nature. However, it never distinguishes itself from the sibling tools wan_generate_video and wan_generate_video_from_image, leaving the agent to guess which generator to call.

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?

There is no explicit when-to-use or when-not-to-use guidance and no mention of alternatives, despite three sibling video-generation tools. At best the input-mode list implies 'use this for any input type', but that inference is left entirely to the agent.

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 behavioral burden. It discloses one useful trait: the call returns a Task ID, implying asynchronous generation. However, it says nothing about cost, latency, expected polling via wan_get_task, permission requirements, or which parameters are model-gated, which are critical for a long-running generation call.

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 purpose sentence is front-loaded and the model bullets are scannable, with no filler text. The 'Returns' block is somewhat redundant given an output schema exists, but the overall structure is tight and readable.

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 17-parameter, multi-model generative tool with no annotations, the description is thin: it omits the async polling workflow, model-to-parameter dependencies, and cost/credit implications. An output schema exists, so the return summary is somewhat redundant. Adequate but with clear gaps for a tool of this complexity.

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 baseline is 3. The description does add meaning for three models, but it omits two enum values ('wan2.6-t2v', 'wan3.0-video') and doesn't explain how model choice constrains other params (e.g., reference_video_urls, audio_url, media). Marginal value beyond what the schema already documents.

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 states a specific verb and resource ('Generate AI video from a reference image') and names the Wan i2v model family. It does not differentiate this tool from the sibling 'wan_generate_video' or 'wan_generate_video_all_in_one', so an agent must infer which one to pick. The model list also oddly includes 'wan2.6-r2v' (reference video-to-video), which blurs the tool's stated image-input scope.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

No when-to-use, when-not-to-use, or alternative-tool guidance is given, despite three closely related video-generation siblings. It doesn't note that wan2.6-r2v requires reference_video_urls rather than an image, nor when to prefer the all-in-one sibling. The agent gets model options but no routing logic.

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 describes the task states (pending, processing, completed, failed) and indicates the tool returns status and result URLs. It does not cover rate limits or polling behavior, but for a status-checking tool, this is adequate and not misleading.

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 well-structured, with a clear purpose statement followed by bullet points for task states. Every sentence adds value, and there is no extraneous 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 output schema exists (not shown but indicated), the description appropriately summarizes the return type (status and video URLs). The sibling tools list provides sufficient context for understanding the tool's role in the broader workflow.

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 single parameter 'task_id' is described in the schema, but the description adds valuable context: 'The task ID returned from a generation request. This is the 'task_id' field from any wan_generate_* tool response.' This clarifies the origin and usage beyond the schema's 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 that the tool queries the status and result of a video generation task. It is well differentiated from sibling tools like wan_generate_video (which creates tasks) and wan_get_tasks_batch (which retrieves multiple tasks).

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 explicitly states to use this tool to check if a generation is complete and retrieve results. It also lists task states, providing guidance on when to call it. However, it does not explicitly state when not to use or mention alternatives beyond the context.

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

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

No annotations are provided, so the description carries the burden. It states the return includes status and video information, which is basic. It does not disclose rate limits, authentication needs, or any side effects. The mention of max batch size is in the schema description, not the main description.

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 three sentences: purpose, benefit, and return. It is front-loaded and concise, with no unnecessary 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 tool's simplicity (one parameter, output schema exists), the description sufficiently covers what the tool does and returns. The output schema handles return value details, so no further elaboration 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% for the single parameter 'task_ids', with a clear description of its purpose and constraints. The main description adds no additional semantic value beyond what the schema already provides, meeting the baseline for high 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 uses the specific verb 'query' and resource 'multiple video generation tasks', clearly distinguishing it from the sibling tool 'wan_get_task' which queries a single task. The phrase 'at once' and 'batch' reinforce the batching behavior.

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 checking multiple tasks efficiently, and the sibling tool names provide clear contrast with 'wan_get_task' for single tasks. However, it does not explicitly state when not to use or mention alternative scenarios.

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?

The description says it returns a categorized list. No annotations exist, so the description carries the burden. It's adequate but doesn't add extra behavioral traits beyond listing.

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?

Three sentences, no fluff. First sentence states purpose, second adds context, third describes output. Every sentence 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 simple listing tool with no parameters and an output schema present, the description is complete. It tells what it does and what it returns.

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?

No parameters exist in the schema. Baseline for 0 params is 4. The description adds no parameter info, but none is needed.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states it lists all available Wan API actions and corresponding tools, serving as a reference guide. This distinguishes it from sibling tools like wan_generate_video or wan_get_task.

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?

Usage is implied as a reference to discover available actions and tools. No explicit when-not or alternatives, but the context of siblings makes it clear.

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 responsibility for behavioral disclosure. It only states that it lists models and returns a table, without mentioning any constraints, permissions, or side effects. The read-only nature is implied but not explicit.

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 exceptionally concise with only three sentences. The first sentence front-loads the purpose, followed by additional detail. Every sentence contributes meaning without redundancy.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a simple listing tool with an output schema, the description adequately covers what the tool does and what it returns. It does not mention any error conditions or prerequisites, but given the tool's simplicity, it is mostly complete.

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?

There are zero parameters, so the schema coverage is complete. The description adds value by explaining the return format (table with descriptions and constraints), which is not captured in the empty input schema. Baseline 4 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 tool lists all available Wan video generation models and specifies what information it shows (capabilities, supported actions, use cases). The verb-resource pair is specific, and the tool is well differentiated from sibling tools that handle generation or list other entities.

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 using this tool before generating videos, but it does not explicitly state when not to use it or mention alternative tools. Given the distinct sibling tools, the context is clear, but explicit guidance is missing.

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?

The description indicates a read-only operation (list, shows) and specifies the return format (table of resolutions with descriptions). Despite no annotations, it effectively communicates behavior without contradictions.

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?

Three concise sentences front-load the purpose, followed by details on content and return format. No superfluous 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 parameterless tool with an output schema, the description fully covers the purpose and return structure. No gaps remain.

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 zero parameters in the input schema, the description does not need to explain parameters. It adds value by clarifying scope ('all available') and the nature of returns.

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 'List all available resolutions for Wan video generation', specifying the verb (list) and resource (resolutions). It distinguishes from sibling tools like wan_generate_video by focusing on listing options rather than generation.

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 to retrieve resolution options but provides no explicit guidance on when to use this tool versus alternatives, such as before generating a video to select a resolution.

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