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runapi-ai

RunAPI Midjourney MCP Server

by runapi-ai

edit_image

Create a Midjourney image-editing task on RunAPI with a prompt and source image URL; receive task ID, status, and output URLs.

Instructions

Create a Midjourney task on RunAPI (edit image). Returns a task id, status, and output URLs.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
waitNoPoll until the task reaches a terminal status.
modelNoRunAPI model slug for this model line.
promptYesDeclared type: string.
mask_urlNoDeclared type: string.
timeout_msNo
callback_urlNoDeclared type: string.
poll_interval_msNo
source_image_urlYesDeclared type: string.
include_split_imagesNoDeclared type: boolean.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed9 schema fields changedv0.4.0
    • changedInput schema / additionalProperties
      Previous value: -falseNew value: +{}
    • addedInput schema / properties / callback_url / description
      Added value: +"Declared type: string."
    • addedInput schema / properties / include_split_images / description
      Added value: +"Declared type: boolean."
    • addedInput schema / properties / mask_url / description
      Added value: +"Declared type: string."
    • removedInput schema / properties / model / enum
      Removed value: -[
      -  "midjourney-edit-image"
      -]
    • addedInput schema / properties / poll_interval_ms / maximum
      Added value: +9007199254740991
    • addedInput schema / properties / prompt / description
      Added value: +"Declared type: string."
    • addedInput schema / properties / source_image_url / description
      Added value: +"Declared type: string."
    • addedInput schema / properties / timeout_ms / maximum
      Added value: +9007199254740991
  2. First observedv0.3.0

TDQS

B3/5.0
Behavior3/5

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

No annotations are provided, so the description carries the full burden. It does disclose the async task model and the return shape (task id, status, output URLs), which is genuinely useful, but it omits whether the operation is billed, what permissions/auth are required, and how the wait/polling workflow behaves.

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?

Two tight sentences with the action front-loaded and no filler. It is efficient, though the parenthetical '(edit image)' is a slightly thin substitute for real scoping information.

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

Completeness3/5

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

The description compensates for the missing output schema by summarizing return values, which is a real contribution. However, for a 9-parameter async task tool with no annotations, the absence of usage routing and parameter meaning leaves meaningful gaps.

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

Parameters2/5

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

Schema coverage is 78%, near the baseline-3 threshold, but the description adds nothing about any parameter. Critically, the required prompt and source_image_url are documented only as 'Declared type: string', and mask_url — central to an image-editing tool — is never explained in either the schema or the description.

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 names a specific verb (Create) and resource (Midjourney task on RunAPI) and qualifies it with '(edit image)', which separates it from text_to_image and image_to_video. It stops short of explicitly differentiating from the other image siblings, but the core purpose is unambiguous.

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 guidance and no mention of alternatives such as text_to_image (no source image) or image_to_video (video output). An agent must infer the selection criteria purely from the tool name.

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