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 only mentions the return type (task ID and video info) but does not disclose any side effects, authentication needs, rate limits, or resource implications. For a generation tool, 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 extremely concise with three sentences, each adding value. It front-loads the purpose and model, then offers alternative direction, and finally states returns. No wasted words.

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

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

Given the tool's complexity (10 parameters, output schema exists), the description is fairly complete. It specifies the model, distinguishes alternative tools, and notes the output, which is sufficient when combined with the schema. Minor gap: no mention of typical usage patterns or prompt guidance, but the schema covers prompt 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?

The input schema has 100% coverage with descriptions for all 10 parameters. The description adds little beyond the schema, only referencing the prompt implicitly. Baseline 3 is appropriate as the schema already provides adequate meaning.

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 generates AI video from a text prompt using the Wan text-to-video model, specifically wan2.6-t2v. It distinguishes itself from the sibling tool wan_generate_video_from_image by explicitly noting the image-based alternative.

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 (text-to-video generation) and directs users to the sibling tool for image-to-video. However, it does not mention when not to use it or any prerequisites, limiting full guidance.

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, the description must carry behavioral disclosure. It does mention that the tool returns a Task ID and video information including URLs and state, and it hints at async behavior via the 'callback_url' parameter. However, it does not explain polling workflows, typical generation times, timeout implications, or failure modes—gaps for a long-running generation 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 front-loaded with a clear first sentence, followed by a structured list of models and a return value line. It is appropriately sized for the tool's complexity, though the model list slightly duplicates the schema's model enum, preventing a perfect score.

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?

The description covers the return value format and model choices, and an output schema exists to handle structured return data. It falls short on explicitly guiding the user through the asynchronous nature and when to use this versus sibling tools, but it still provides enough context for a generation task with a rich 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?

The input schema covers 100% of the 14 parameters with descriptive text, so the description does not need to repeat parameter semantics. It adds value by explaining model options, but this does not go beyond what the schema already provides for individual 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 first sentence uses a specific verb ('Generate') and resource ('AI video from a reference image'), immediately clarifying the tool's core function. It also explicitly names 'Wan image-to-video models' and lists three distinct model variants, which differentiates it from the sibling 'wan_generate_video' (likely text-to-video).

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 by listing the three model options with their intended use cases (standard, reference-video, fast). However, it does not explicitly state when to choose this tool over 'wan_generate_video' or other siblings, leaving that exclusion implicit.

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.