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by io.github.RightOnPar-LLC
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Image Edit

image-edit

Image Edit — Edit an existing image with a text instruction — gpt-image-1 under the hood. Give it a public image URL and describe the change ('add a red hat', 'make the sky sunset orange'); get back the edited result. Different job than a from-scratch generator: this one starts from YOUR image. Input: {image_url: string, prompt: string}. Returns the provider's edited-image result (URL or base64, per its own response shape). (20 MESH/call, a tool · media)

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
inputYesPayload for image-edit

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed4 schema fields changed
    • addedInput schema / properties / input / additionalProperties
      Added value: +false
    • changedInput schema / properties / input / description
      Previous value: -"Capability-specific payload, e.g. agent-brain: {think:'...'}; agent-memory: {action:'store'|'recall', content|query}"New value: +"Payload for image-edit"
    • addedInput schema / properties / input / properties
      Added value: +{
+  "image_url": {
+    "description": "REQUIRED — a public http(s) URL of the source image to edit.",
+    "format": "uri",
+    "type": "string"
+  },
+  "prompt": {
+    "description": "REQUIRED — a text instruction describing the edit to make, e.g. 'add a red hat' or 'make the sky sunset orange'.",
+    "minLength": 1,
+    "type": "string"
+  }
+}
    • addedInput schema / properties / input / required
      Added value: +[
+  "image_url",
+  "prompt"
+]
  2. Changed4 schema fields changed
    • removedInput schema / properties / input / additionalProperties
      Removed value: -false
    • changedInput schema / properties / input / description
      Previous value: -"Payload for image-edit"New value: +"Capability-specific payload, e.g. agent-brain: {think:'...'}; agent-memory: {action:'store'|'recall', content|query}"
    • removedInput schema / properties / input / properties
      Removed value: -{
-  "image_url": {
-    "description": "REQUIRED — a public http(s) URL of the source image to edit.",
-    "format": "uri",
-    "type": "string"
-  },
-  "prompt": {
-    "description": "REQUIRED — a text instruction describing the edit to make, e.g. 'add a red hat' or 'make the sky sunset orange'.",
-    "minLength": 1,
-    "type": "string"
-  }
-}
    • removedInput schema / properties / input / required
      Removed value: -[
-  "image_url",
-  "prompt"
-]
  3. Added

TDQS

A4.1/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=false, destructiveHint=false, and openWorldHint=true, and the description adds useful context beyond them: the underlying provider, the cost per call, and the return shape ('URL or base64'). It does not fully clarify whether the original image is mutated or a new result is returned, but 'get back the edited result' implies a non-destructive transformation.

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 description is compact and front-loaded, with the core purpose stated first, followed by usage, input shape, output, and cost. Minor noise like '(20 MESH/call, a tool · media)' is somewhat tangential, but overall every major sentence 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?

For a tool with high schema coverage, no output schema, and a moderate behavioral profile, this description covers purpose, input, output format, pricing, and the distinction from generation. It could add error or format constraints, but the agent has enough to invoke the tool correctly and interpret the result.

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%, and nested parameters already document image_url and prompt clearly. The description reinforces this with concrete examples like 'add a red hat' and 'make the sky sunset orange,' but does not add significant new parameter-level meaning beyond the schema.

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 states a specific verb and resource: 'Edit an existing image with a text instruction,' and clarifies the underlying model is gpt-image-1. It also distinguishes itself from a from-scratch generator by emphasizing 'this one starts from YOUR image,' making its role unambiguous among media siblings.

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 explicitly tells the agent when to use the tool: when there is an existing image URL and a text change to apply. It also gives a clear when-not by contrasting with 'a from-scratch generator,' although it does not name the alternative sibling tool directly.

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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