image-to-image

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 states the tool performs image-to-image generation using a specific model, implying a generative/mutation operation, but doesn't disclose critical traits like whether it's read-only or destructive, authentication requirements, rate limits, output format, or error handling. For a tool with 5 parameters and no 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 in Chinese that directly states the tool's core function and model constraint. It's appropriately sized and front-loaded with the essential information, with no wasted words. However, it could be slightly more structured by explicitly separating purpose from constraints for better readability.

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 (5 parameters, nested objects, no output schema, and no annotations), the description is incomplete. It lacks information about the output (e.g., image format, URL structure), error conditions, usage limits, and how it differs from sibling tools. For a generative tool with multiple parameters and platform-specific constraints, more context is needed to help the agent use it effectively.

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 description adds no parameter-specific information beyond what's already in the input schema, which has 100% schema description coverage. The schema thoroughly documents all 5 parameters (prompt, imageUrl, ratio, localPath, model) with descriptions, constraints, and enums. Since the schema does the heavy lifting, the baseline score of 3 is appropriate, as the description doesn't compensate with additional semantic context.

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

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

The description clearly states the tool's purpose as '使用火山方舟 Seedream 模型进行图生图（Ark-only）', which translates to 'Use Volcano Ark Seedream model for image-to-image (Ark-only)'. This specifies the verb ('进行图生图' - perform image-to-image), resource ('火山方舟 Seedream 模型' - Volcano Ark Seedream model), and platform constraint ('Ark-only'). It distinguishes from siblings like 'text-to-image' by focusing on image-to-image transformation rather than text-based generation. However, it doesn't explicitly differentiate from 'generate-img-seedream4' which might be similar.

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 mentions 'Ark-only', which might imply a platform restriction, but doesn't explain when to choose this over other image generation tools like 'generate-image', 'generate-img-seedream4', or 'text-to-image'. There's no mention of prerequisites, use cases, 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.