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生成图片(同步)

generate_image

Generate images synchronously through Labnana's API, supporting Gemini, GPT-Image-2, Wan2.7, and Seedream models. Pass reference images for image-to-image edits. Returns base64 data and metadata.

Instructions

同步生成图片,直接返回 base64 图片数据与生成元数据。支持 Gemini 系列、GPT-Image-2、Wan2.7、Seedream 5.0 Pro。可传参考图片(fileUri 或 base64 inlineData)做图生图、改图。对应 POST /openapi/v1/images/generation。注意:同步接口返回体积大,4K 图片建议改用 generate_image_async。

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior4/5

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

With no annotations, the description carries the full burden. It discloses key behaviors: synchronous execution, direct return of base64 data and metadata, support for multiple models, and a warning about high return volume. It does not cover errors or authentication, but the provided details are substantial and useful.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is compact and well-structured: a single sentence covering core functionality, followed by a short warning about async alternatives. Every clause adds value, and the critical distinction (synchronous vs async) is front-loaded.

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

Completeness4/5

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

For a complex tool with no output schema, the description covers the return value (base64 data and metadata), supported models, reference image support, and the API endpoint. It misses explicit parameter names for model selection and error handling, but overall it is fairly complete for invocation.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema has zero parameters, so the description is the only source of parameter info. It explicitly mentions 'fileUri 或 base64 inlineData' for reference images and lists supported models, adding meaning beyond the empty schema. Given the 0-param baseline of 4, this extra detail qualifies for a 5.

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

Purpose5/5

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

The description states '同步生成图片,直接返回 base64 图片数据与生成元数据' clearly defining the tool's function. It differentiates from sibling 'generate_image_async' by emphasizing the synchronous nature and explicitly warns about large payloads, naming the alternative.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides explicit when-to-use guidance: use this for synchronous generation and switch to generate_image_async for 4K images due to large return volume. It also mentions support for reference images, indicating use cases like image-to-image and editing.

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

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