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eroq — AI video, images, voice and music

Generate an image

generate_image

Render one image from a text prompt. Costs credits per render — models: eroq-uncensored (10 cr), eroq-krea2 (10 cr), eroq-one (10 cr), eroq-anime (10 cr), eroq-flash-image (10 cr), eroq-flux-image (12 cr), gpt-image-2-5-sunburst (31 cr, creator+), gpt-image-2-5-flare (31 cr, creator+), nano-banana-2-1 (31 cr, creator+), seedream-5-0-pro (36 cr, creator+), nano-banana-pro (124 cr, creator+), flux-3-image (32 cr, hobby+), grok-imagine-2-0 (32 cr, hobby+). Waits for the render and returns the picture itself plus a link to it (kept in the eroq library).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
modelNoImage model id (default eroq-uncensored).
aspectNoFrame format (default 1:1) — same price for every ratio.
promptYesWhat to render — subject, composition, light, mood, in one flowing paragraph.
negative_promptNoWhat to avoid in the image.
reference_image_urlNoHTTPS photo URL — the engine holds this face/subject across the render.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.1/5.0
Behavior4/5

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

With no annotations provided, the description carries the full burden and does well: it discloses that renders cost credits, enumerates per-model credit costs and tier gates (creator+/hobby+), and states that it blocks until the render completes and returns the picture plus a stored link. It omits auth/error behavior, keeping it just short of a 5.

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 core sentence is front-loaded and the return-behavior sentence is useful. The inline model enumeration is long and somewhat list-heavy, but each entry (cost + tier) carries information an agent needs for selection, so it largely earns its place.

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?

There is no output schema, and the description compensates by describing the return (the image plus a link kept in the eroq library). Combined with cost, model options, and blocking semantics, it is nearly complete for a 5-parameter generation tool, with only auth/error details missing.

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

Parameters4/5

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

Schema description coverage is 100%, so the baseline is 3. The description goes beyond the schema by attaching credit cost and access-tier information to each model enum value, which the schema itself does not provide, adding real decision value for model choice.

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?

States a specific verb and resource ('Render one image from a text prompt') and bounds the scope to a single image. This clearly distinguishes it from siblings like generate_video, generate_music, and get_image_status, which polls rather than renders.

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

Usage Guidelines3/5

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

The use case (produce an image from a prompt) is implied by the description, but it never states when to prefer this over siblings such as make_edit, enhance_prompt, or run_flow, nor when not to use it. Usage is inferable but not spelled out.

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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