Galatic.ai

Edit image

edit_image

Edit an existing image. mode "edit" applies prompt as a free-text instruction and always runs flux-kontext-pro (model is ignored). mode "improve" restyles the image with a preset (improvementType; prompt is ignored) on flux-kontext-pro or nano-banana-edit. image_url must be an image from this app: the publicUrl or presignedGetUrl returned by upload_media (after the PUT), or the contentUrl of an item from list_gallery or fetch, or a previous generation. Any other host is refused (400) without spending credits. Spends Galatic.AI credits. Returns a job id; poll get_job_status with kind "image".

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>",
      "image_url": "<image_url>",
      "context": "<context>",
      "llm_model": "<llm_model>"
    },
    "name": "edit_image"
  }
}

Input Schema

Name	Required	Description	Default
`mode`	No		edit
`model`	No
`prompt`	Yes
`context`	Yes	Explain in 15-25 words, in third person, why this tool is called and how it supports the user's goal. For analytics only. You MUST describe only the abstract purpose of the tool call. NEVER include, repeat, paraphrase, or infer personal, sensitive, or identifying information from the user request or tool results, including names, emails, phone numbers, IPs, IDs, or credentials. You MUST generalize specific entities into roles such as "a user", "the customer", or "an account". Example: "Retrieving a customer's recent orders to investigate a billing issue and help support determine the appropriate resolution."
`image_url`	Yes
`llm_model`	Yes	The exact model identifier you (the assistant) are running as, taken from your system prompt or environment (e.g. "claude-opus-4-8", "gpt-5.2"). Used for analytics only. If you do not know your model identifier with certainty, pass "unknown" — never guess.
`conversation_id`	No	Pass the exact conversation_id from the server's previous response, unchanged. The server provides it on the first call — never invent one, and do not issue parallel tool calls until you have it. Keep passing the same conversation_id for the rest of the conversation, including after later user messages or on a different task; do not reset it when the user starts a new request.
`improvementType`	No

Schema Changelog

Changes observed during successful MCP inspections.

First observedOct 24, 2026

TDQS

A4.4/5.0

Behavior3/5

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

Annotations already declare the mutation profile (readOnlyHint=false, destructiveHint=false, idempotentHint=false, openWorldHint=false). The description adds genuinely useful context beyond that: it spends Galatic.AI credits, off-host URLs are refused with a 400 without spending credits, and it returns a job id rather than a finished image. It does not cover retry/idempotency behavior or what happens to credits on a failed job, so it is good but not exhaustive.

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?

Front-loaded with the core action and mode split, then constraints, then cost and return path. Dense but every sentence carries information. Slightly run-on with parenthetical asides, which costs a point on readability but not on substance.

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

Completeness5/5

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

No output schema exists, yet the description explains the return value (a job id) and the exact follow-up call needed to retrieve results. Combined with the credit cost, URL provenance rules, and mode behavior, an agent has everything needed to invoke this correctly on the first try.

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

Parameters4/5

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

Schema coverage is only 38%, so the description must compensate, and it does for the key fields: mode semantics, model being ignored in edit mode, prompt being ignored in improve mode, improvementType as a preset, and the accepted provenance of image_url. The three analytics parameters (context, llm_model, conversation_id) are left to their schema descriptions, which is reasonable since those are already documented.

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?

States a specific verb and resource ("Edit an existing image") and immediately disambiguates the two modes: "edit" applies a free-text prompt, "improve" applies a preset. An agent can distinguish this from generate_image or improve_prompt without opening the schema.

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

Usage Guidelines5/5

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

Explicitly says which parameter is honored in each mode (model ignored in edit, prompt ignored in improve) and enumerates the only acceptable sources for image_url, naming the sibling tools that produce them (upload_media, list_gallery, fetch). It also routes the agent to get_job_status with kind "image" for the result.

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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