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Krea MCP Server

Krea MCP Server

by keugenek
OverviewSchemaSourceRelated ServersDiscussionsAdmin
JavaScript
Remote

generate_image

Generate an image from a text prompt using AI models like Flux, Ideogram, or Krea. Customize dimensions, style, or use a source image for image-to-image generation.

Instructions

Generate an image using Krea AI. Returns a job_id - use get_job to check status and get the result URL.

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

NameRequiredDescriptionDefault
modelNoModel: flux (default), flux-pro, ideogram, imagen-4, krea-1, chatgpt-image, nano-banana, seedreamflux
widthNoImage width in pixels
heightNoImage height in pixels
promptYesText description of the image to generate
style_idNoOptional style ID to apply
image_urlNoOptional source image URL for image-to-image generation
negative_promptNoWhat to avoid in the image

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.2

TDQS

A4.2/5.0
Behavior4/5

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

With no annotations, the description carries full burden. It discloses that the tool returns a job_id (async) and that another tool is needed to retrieve the final result, which is key behavioral context.

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?

Two sentences, no fluff, front-loaded with purpose. Every sentence adds value.

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 7 parameters and no output schema, the description explains the async return pattern and points to the status tool. It could mention polling, but the instruction to use get_job is sufficient.

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 coverage is 100%, so parameters are already documented. The description does not add parameter-level details beyond the schema, meeting the baseline.

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 uses a specific verb ('generate') and resource ('image'), explicitly names the platform ('Krea AI'), and distinguishes from siblings like generate_video by focusing on image generation.

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

Usage Guidelines4/5

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

It indicates the async workflow by mentioning job_id and directing to get_job for results. It does not explicitly state when not to use, but the sibling context implies alternatives for video.

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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