@runapi.ai/gpt-image-2-mcp

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edit_image

Edit images by submitting a prompt and source image URLs to create a GPT Image 2 task on RunAPI, returning task ID, status, and output URLs.

Instructions

Create a GPT Image 2 task on RunAPI (edit image). Returns a task id, status, and output URLs.

Input Schema

Name	Required	Description
`wait`	No	Poll until the task reaches a terminal status.
`model`	No	RunAPI model slug for this model line.
`prompt`	Yes	Declared type: string.
`background`	No	Declared type: string. Known values: "transparent", "opaque", "auto".
`timeout_ms`	No
`aspect_ratio`	No	Declared type: string. Known values: "auto", "1:1", "3:2", "2:3", "4:3", "3:4", "5:4", "4:5", "16:9", "9:16", "2:1", "1:2", "3:1", "1:3", "21:9", "9:21".
`callback_url`	No	Declared type: string.
`poll_interval_ms`	No
`output_resolution`	No	Declared type: string. Known values: "1k", "2k", "4k".
`source_image_urls`	Yes	Declared type: array.

Schema Changelog

Changes observed during successful MCP inspections.

Changed1 schema field changedv0.3.0Oct 19, 2026

addedInput schema / properties / background

Added value: +{
+  "description": "Declared type: string. Known values: \"transparent\", \"opaque\", \"auto\".",
+  "type": "string"
+}

Changed15 schema fields changedv0.2.0Oct 5, 2026

changedInput schema / additionalProperties
```
Previous value: -falseNew value: +{}
```

addedInput schema / properties / aspect_ratio / description

Added value: +"Declared type: string. Known values: \"auto\", \"1:1\", \"3:2\", \"2:3\", \"4:3\", \"3:4\", \"5:4\", \"4:5\", \"16:9\", \"9:16\", \"2:1\", \"1:2\", \"3:1\", \"1:3\", \"21:9\", \"9:21\"."

removedInput schema / properties / aspect_ratio / enum

Removed value: -[
-  "auto",
-  "1:1",
-  "3:2",
-  "2:3",
-  "4:3",
-  "3:4",
-  "5:4",
-  "4:5",
-  "16:9",
-  "9:16",
-  "2:1",
-  "1:2",
-  "3:1",
-  "1:3",
-  "21:9",
-  "9:21"
-]

addedInput schema / properties / callback_url

Added value: +{
+  "description": "Declared type: string.",
+  "type": "string"
+}

removedInput schema / properties / model / enum
```
Removed value: -[
-  "gpt-image-2"
-]
```

addedInput schema / properties / output_resolution / description

Added value: +"Declared type: string. Known values: \"1k\", \"2k\", \"4k\"."

removedInput schema / properties / output_resolution / enum
```
Removed value: -[
-  "1k",
-  "2k",
-  "4k"
-]
```
addedInput schema / properties / poll_interval_ms / maximum
```
Added value: +9007199254740991
```
addedInput schema / properties / prompt / description
```
Added value: +"Declared type: string."
```
addedInput schema / properties / prompt / type
```
Added value: +"string"
```
addedInput schema / properties / source_image_urls / description
```
Added value: +"Declared type: array."
```
addedInput schema / properties / source_image_urls / items
```
Added value: +{}
```
addedInput schema / properties / source_image_urls / type
```
Added value: +"array"
```
addedInput schema / properties / timeout_ms / maximum
```
Added value: +9007199254740991
```

addedInput schema / required

Added value: +[
+  "prompt",
+  "source_image_urls"
+]

First observedv0.1.0Jun 24, 2026

TDQS

B3.1/5.0

Behavior2/5

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

No annotations are provided, so the description carries the full behavioral burden. It discloses that the tool creates a task and returns a task id, status, and output URLs, but it does not explain permissions, side effects, cost, or how async/polling behavior works beyond the schema.

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 tightly packed sentence with the core action front-loaded. It includes purpose and return behavior without wasted wording.

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?

For a 10-parameter task-creation tool with no annotations and no output schema, the description covers the basic action and return shape. However, it omits important context about async execution, wait/polling behavior, and when editing is appropriate compared with sibling tools.

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 high at 80%, so the schema already documents most parameters. The description adds no parameter-level semantics, which is acceptable at baseline but does not compensate for the few undocumented fields.

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 gives a specific verb and resource: create a GPT Image 2 task for editing an image on RunAPI. The parenthetical '(edit image)' helps distinguish it from text_to_image, though it does not explicitly state that distinction.

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 says what the tool does but gives no guidance on when to use it versus siblings such as text_to_image or get_task. There are no stated preconditions, exclusions, or alternative-selection cues.

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

Deploy Server

edit_image

Instructions

Input Schema

Schema Changelog

TDQS

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