Omnia Media MCP

generate_image

Generate photorealistic images from text prompts using FLUX.1 diffusion models, producing high-fidelity visuals in about 2 seconds with configurable aspect ratio and step count.

Instructions

Generate high-fidelity photorealistic images using FLUX.1 diffusion models on the Runware edge cluster in ~1.5 to 2.5 seconds.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "prompt": "<prompt>"
    },
    "name": "generate_image"
  }
}

Input Schema

Name	Required	Description
`model`	No	Target model ID. Defaults to 'runware:100@1' (FLUX.1 Schnell).
`steps`	No	Inference step count (e.g. 4 for Schnell, 25 for Dev). Defaults to 4.
`prompt`	Yes	Detailed descriptive visual prompt for diffusion synthesis.
`aspect_ratio`	No	Image aspect ratio. Defaults to 1:1.

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Nov 6, 2026

TDQS

B3.1/5.0

Behavior3/5

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

With no annotations, the description must carry the full behavioral burden; it does disclose the infrastructure (Runware edge cluster) and expected latency of ~1.5-2.5 seconds, which is genuinely useful. However, it says nothing about cost/credits, rate limits, prompt moderation, determinism/seed behavior, or what the tool actually returns (URL, binary, or saved asset) — significant gaps for an image-generation tool.

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?

A single front-loaded sentence with no filler, correctly leading with the verb and output. The performance figure is somewhat promotional, but it is at least factual and short.

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 generation tool with no annotations and no output schema, the description should explain what comes back and any operational constraints. It covers what is generated and how fast, but leaves the return value, side effects, and cost model entirely unspecified.

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%, so the baseline is 3; the schema already documents every parameter including defaults and the enum. The description adds only model-family framing ('FLUX.1 diffusion models'), which gives context for interpreting 'steps' and 'model' but no new per-parameter semantics.

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?

States a specific verb ('Generate') and resource ('photorealistic images') plus the underlying model family and runtime, so the agent knows exactly what it produces. It does not mention any siblings (batch_render_catalog, stage_product, recreate_from_seed), so the boundary between this and batch or product-staging tools 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.

Usage Guidelines2/5

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

There is no when-to-use or when-not guidance and no mention of the sibling tools at all. An agent cannot tell from this text whether to call generate_image, batch_render_catalog, or stage_product for a given image task.

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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