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coeus_image_generate

Generate image bytes from a prompt when a capable provider is configured. Returns an error if the routed model cannot emit an image.

Instructions

Generate image bytes only when a capable provider is configured; returns an error if the routed model cannot emit an image

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
seedNoSeed
modelNoModel
styleNoStyle
promptYesPrompt
num_imagesNoNum images
aspect_ratioNoAspect ratio
return_formatNoReturn format
negative_promptNoNegative prompt

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.1.0

TDQS

C2.9/5.0
Behavior2/5

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. It mentions that the tool returns an error if the routed model cannot emit an image, which is useful, but it omits many other behavioral aspects: it doesn't state whether the operation is read-only or mutating (though generation is typically non-destructive), doesn't describe the output format beyond 'image bytes', doesn't mention rate limits, auth requirements, or side effects. For a generation tool, this is sparse.

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 a single sentence with no wasted words. It front-loads the core purpose ('Generate image bytes') and then adds the conditional context. It is as concise as possible while conveying the key operational constraint.

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

Completeness2/5

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

For a tool with 8 parameters, no output schema, and no annotations, the description is insufficient. It doesn't explain parameter behavior beyond the schema, doesn't describe the return format in detail (e.g., MIME type, encoding), doesn't provide examples or edge cases, and doesn't clarify how the tool interacts with routing. An agent would struggle to invoke it correctly for varied use cases.

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

Parameters3/5

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

Schema description coverage is 100% for all 8 parameters, but the schema descriptions are terse (e.g., 'Seed', 'Model', 'Style'). The tool description adds no additional meaning to any parameter, such as clarifying what 'style' accepts or how 'aspect_ratio' should be formatted. Since the schema covers all parameters, the baseline is 3, and the description doesn't enhance it.

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

Purpose4/5

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

The description clearly states the tool generates image bytes, with a specific verb and resource. It also adds a conditional qualifier about provider capability. However, it doesn't explicitly distinguish itself from sibling tools like coeus_pixel_art_generate, which could lead to ambiguity for an agent choosing between image generation tools.

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 gives a condition for successful use (capable provider configured) and mentions an error case, but it provides no guidance on when to choose this tool over alternatives. It doesn't reference sibling tools or specify use cases such as 'for general images vs. pixel art'. An agent would have no clear decision criteria beyond the provider capability.

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