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PixelForge MCP

PixelForge MCP

by Tehnolabs
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Remote

Compare Images

compare_images

Compare two or more images and analyze differences for A/B testing, design review, before/after comparison, and change detection. Identify visual changes and support clear review decisions.

Instructions

Compare two or more images and analyze differences.

Useful for A/B testing, design review, before/after comparison, and change detection.

Input Schema

NameRequiredDescriptionDefault
modelNoAnalysis model override. Default gemini-3.5-flash-lite. Use gemini-3.8-flash for harder OCR or reasoning.
promptNoComparison focus (e.g. "color differences"). Default: general comparison.
image_pathsYesPaths to 2+ images to compare
use_groundingNoEnable Google Search grounding for more accurate, factual results.

Output Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changedv0.6.0
    • addedInput schema / properties / model
      Added value: +{
+  "anyOf": [
+    {
+      "type": "string"
+    },
+    {
+      "type": "null"
+    }
+  ],
+  "default": null,
+  "description": "Analysis model override. Default gemini-3.5-flash-lite.\nUse gemini-3.8-flash for harder OCR or reasoning."
+}
  2. First observedv0.5.4

TDQS

B3.2/5.0
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 does not say whether the call is read-only versus mutating, whether it consumes paid model credits (relevant given the estimate_cost sibling), what permissions are needed, or what form the analysis output takes beyond the bare phrase 'analyze differences'.

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?

Two sentences, purpose front-loaded, with a compact use-case list that follows. No filler, though the use-case list is somewhat generic relative to the tool's capability.

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?

An output schema exists, so return values need not be described. However, with zero annotations the description should have covered safety/mutation semantics and cost implications, which are missing; the enumerated use cases only partially compensate.

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 all four parameters (model, prompt, image_paths, use_grounding) are already documented in the schema. The description adds no parameter-level guidance such as how to phrase the prompt or when grounding is worth enabling, so baseline 3 applies.

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?

States a specific verb and resource ('compare two or more images') plus the action taken ('analyze differences'), so the operation is immediately understandable. It does not distinguish itself from the sibling analyze_image, which likely handles single-image inspection, leaving the boundary implicit.

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

Usage Guidelines3/5

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

The description lists broad application scenarios (A/B testing, design review, before/after, change detection), which implies when the tool is appropriate. It never states when to choose it over analyze_image or edit_image, and offers no exclusions or prerequisites.

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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