Generate Image from Text

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

Beyond the annotations, the description discloses several important runtime behaviors: 30-60 second latency, streaming, a default of 4 images per prompt, configurable watermark/download defaults, and the return payload (URLs, local paths, performance timing). This is valuable context that annotations alone do not provide.

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 long but well-organized into clear sections: warning, primary use, args, returns, best practices, examples, and tips. The critical timeout warning is front-loaded at the very top, and each section earns its place by helping the agent craft better prompts and avoid pitfalls.

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 is complete for a generation tool of this complexity. It covers all parameters, the return format, the default behaviors, prompt guidance with examples, and the key environment-specific limitation. There is no obvious missing information an agent would need to call this tool successfully.

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 already covers all 6 parameters with 100% description coverage, so the baseline is 3. The description adds meaningful value above that by explaining prompt structuring (Subject + Action + Environment), style keywords, composition tips, text-in-image quoting, and concrete examples. This goes beyond simple parameter names and enum values.

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 function: generating images from a text description using SeeDream 4.5. It further positions it as 'the primary tool for creating images from scratch,' which distinguishes it from the edit/blend/variations siblings in the tool list.

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 guidance is explicit: it names the primary use case, warns that the tool times out on Claude.ai, and directs those users to seedream_submit with mode='text'. This gives the agent a concrete decision rule for choosing between this tool and a specific alternative.

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