g-pic

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, and it does so well: it reveals automatic size adaptation when the API does not support the requested dimensions, the default provider resolution order (GEN_IMAGE_ADAPTER → sensenova), and the default instance behavior (None → main instance). Some behaviors like error handling or rate limits are omitted, but the key operational quirks are disclosed.

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 compact and well-structured: a one-sentence purpose summary followed by a clean parameter list. Every line adds useful information with no filler. The only minor weakness is the reference to an external '3.7 命名实例机制' section, which reduces self-containedness.

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?

This is a moderate-complexity generation tool with 5 parameters and an output schema, so return-value documentation is not required from the description. The description covers all parameter semantics, defaults, provider fallback, and size-adaptation behavior. It lacks explicit usage examples or edge-case handling, but overall it is sufficient for selecting and invoking the tool correctly.

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 0%, so the description must compensate, and it does: every parameter (prompt, size, n, provider, instance) is explained with defaults, valid options or behaviors, and resolution rules. This goes far beyond the bare schema types and titles, giving an agent enough to construct valid calls confidently.

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 identifies a specific operation: general text-to-image generation via the GEN_IMAGE pipeline, with support for multiple images and configurable provider/instance. It is concrete and not a tautology. However, it does not explicitly contrast itself with sibling tools, so some differentiation is left to inference.

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 explicit when-to-use or when-not-to-use guidance is provided. The phrase '通用文生图' implies a general purpose, and the mention of n>1, provider, and instance signals capabilities, but the description never tells an agent when to choose g-pic over siblings like r-pic or i-pic, nor what scenarios would make another tool more appropriate.

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