PixelForge MCP

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Upscale Image

upscale_image

Upscale images to 1K, 2K, or 4K size tiers using a Gemini image model. Re-renders the source at a higher resolution; use resize for exact pixel scaling.

Instructions

Upscale an image to a higher 1K/2K/4K size tier with a Gemini image model.

The source is re-rendered at a higher size tier with an instruction to reproduce it exactly. Fine details can differ from the source; for exact pixel scaling use transform_image(operation="resize").

Input Schema

Name	Required	Description	Default
`model`	No	Image model to use (default gemini-nano-banana-2.1). gemini-3-pro-image is the other 4K-capable option.
`image_path`	Yes	Path to the image to upscale
`output_format`	No	Output format (png, jpeg, webp)	png
`upscale_factor`	No	"x2" (double) or "x4" (quadruple). The output is the smallest of 1K/2K/4K that fits, capped at 4K.	x2
`output_filename`	No	Custom output filename (optional)

Output Schema

Name	Required	Description	Default
No arguments

Schema Changelog

Changes observed during successful MCP inspections.

Changed2 schema fields changedv0.7.0Oct 30, 2026

addedInput schema / properties / model

Added value: +{
+  "anyOf": [
+    {
+      "type": "string"
+    },
+    {
+      "type": "null"
+    }
+  ],
+  "default": null,
+  "description": "Image model to use (default gemini-nano-banana-2.1).\ngemini-3-pro-image is the other 4K-capable option."
+}

changedInput schema / properties / upscale_factor / description

Previous value: -"Scale factor: \"x2\" (double) or \"x4\" (quadruple)"New value: +"\"x2\" (double) or \"x4\" (quadruple). The output is\nthe smallest of 1K/2K/4K that fits, capped at 4K."

First observedv0.5.4Jul 12, 2026

TDQS

A4.4/5.0

Behavior4/5

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

With no annotations, the description carries the burden and delivers the most important non-obvious trait: the result is a generative re-render, so 'fine details can differ from the source' — a crucial caveat an agent could not infer from the schema. It omits cost/latency, whether existing files are overwritten, and where output lands when output_filename is null.

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?

Two short sentences, zero filler, with the core capability front-loaded and the sibling escape hatch placed second. Every clause 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?

An output schema exists so return values need no explanation, and all five parameters are fully described. The only remaining gap for a mutation-style, potentially costly generation tool is side effects (overwrite behavior, cost, latency), which the description does not address.

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 coverage is 100%, so every parameter including the model choice and upscale_factor capping behavior is already documented in structured fields. The description reinforces the size-tier concept but adds no syntax or format detail beyond the schema; 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 and resource ('Upscale an image') plus the mechanism ('re-rendered at a higher size tier with a Gemini image model') and the output tiers (1K/2K/4K). It explicitly contrasts itself with the sibling transform_image, so an agent can distinguish the two without opening either 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?

Names the alternative explicitly with its exact invocation: 'for exact pixel scaling use transform_image(operation="resize")'. The condition selecting each tool (generative upscale vs. deterministic resize) is stated, not inferred.

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