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StockCake

by com.stockcake
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Resize Image (Outpaint)

resize_image

Change an image's aspect ratio by extending the canvas with AI (GPT Image 2.5) — the subject is kept, new area is generated. Charges credits. Waits up to 45 s, then returns a job_id for get_job. Requires a free StockCake account (the host will prompt to sign in).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
imageYesA StockCake image id (from search_images), one of your own edited image ids (from a previous result), or a job_id. Never a URL.
promptYesWhat should fill the extended canvas, e.g. 'extend the sandy beach and sky; keep the subject unchanged, do not crop or stretch'
contextYesIn one sentence, what is the user trying to make or find?
llm_modelYesThe 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.
aspect_ratioYesTarget ratio
conversation_idNoPass 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.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.4/5.0
Behavior5/5

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

Goes well beyond the annotations by disclosing cost ('charges credits'), latency ('waits up to 45 s'), the async job_id return pattern, and an auth prerequisite (free StockCake account, host sign-in prompt). These are exactly the traits annotations do not capture.

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?

Four short sentences with zero filler, and the core action is front-loaded ahead of cost, latency, and auth details. Dense but each clause carries distinct operational information.

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?

For a paid, async, auth-gated mutation tool, the description covers cost, wait time, return contract, follow-up tool, and account requirement; the output schema handles return values. Nothing an agent needs to invoke it correctly is missing.

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%, so parameters like image, prompt, context, llm_model, aspect_ratio, and conversation_id are already documented in the schema. The description adds no parameter-level detail beyond what the schema and its enum provide, so baseline 3 applies.

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 (change aspect ratio by extending the canvas), names the mechanism (AI outpainting, GPT Image 2.5), and clarifies the subject is preserved. This clearly distinguishes it from siblings like upscale_image and generate_image.

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?

Explains the scenario (aspect-ratio change via outpainting) and routes the agent forward, saying the returned job_id is used with get_job. It does not explicitly contrast with upscale_image or generate_image, so an agent must infer which sibling to prefer.

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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