seedream_list_models

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

No annotations provided, so description bears full responsibility. It discloses that the tool returns a formatted table with descriptions, capabilities, and pricing. No mention of rate limits or authentication, but for a simple list operation this is sufficient.

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?

Description is extremely concise: four sentences total, with bulleted usage guidance and a clear return statement. 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 simple list tool with no parameters and an output schema, the description is complete. It covers purpose, usage context, and return format, leaving no significant gaps.

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?

Input schema has no parameters, and schema coverage is 100% (trivially). No parameter details needed; description adds no extra parameter info, but baseline score of 4 is appropriate for zero 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?

Description clearly states the tool lists all available Seedream models with capabilities and pricing. The verb 'list' and resource 'models' are specific, and the tool is distinct from sibling tools like generate, edit, or task retrieval.

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?

Explicitly lists three use cases when to invoke this tool: user asks about available models, need to choose a model, or want to compare capabilities. This provides clear context for the agent.

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