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Metis — Generate Image

generate_image

Generate AI images based on a prompt and automatically save them to your personal knowledge management library.

Instructions

Generate an image using AI and save it to the PKM.

Saves to {pkm_root}/outputs/images/YYYY-MM-DD_[slug].png

Args:
    prompt: Description of the image to generate.
    backend: "gemini" (default) or "huggingface"
    model: "flash" (gemini-3.1-flash-preview-image-generation),
           "imagen" (imagen-4.0-generate-001),
           "flux" (FLUX.1-schnell via HuggingFace)
    width: Image width in pixels (default 1024).
    height: Image height in pixels (default 1024).
    output_filename: Optional custom filename (without extension).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
modelNoflash
widthNo
heightNo
promptYes
backendNogemini
output_filenameNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior3/5

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

With no annotations, the description carries full burden. It discloses the save behavior (path format) and parameter options, but lacks details on potential side effects (e.g., overwriting, costs, rate limits, authentication needs). The transparency is adequate but incomplete.

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

Conciseness4/5

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

The description is front-loaded with a one-line summary and save path, followed by a structured Args block. It is clear and efficient, though the Args formatting is slightly verbose. Overall, good balance of detail and brevity.

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?

Given 6 parameters, 1 required, and no annotations, the description covers all parameters and the output location. An output schema exists, so return value documentation is not needed. However, missing usage guidelines and potential side effects prevent a perfect score. Still, it is largely complete for a generation tool.

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?

Although schema description coverage is 0%, the description provides an 'Args' section that explains each parameter with defaults, allowed values, and example usage. This adds substantial meaning beyond the raw input schema, making parameter selection clear.

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 clearly states 'Generate an image using AI and save it to the PKM,' specifying a verb (generate), resource (image), and destination. The title 'Generate Image' aligns with the purpose, and among siblings there is a distinct 'list_generated_images' tool, so there is no ambiguity.

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

Usage Guidelines2/5

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

The description does not provide any guidance on when to use this tool versus alternatives, nor does it mention when not to use it. No prerequisites or contextual conditions are stated, leaving the agent to infer usage from the parameter description alone.

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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