Compare two base64-encoded images to calculate the visual difference percentage, enabling automated visual regression checks for mobile UI testing.
Compare two base64 images and return diff percentage.
| Name | Required | Description | Default |
|---|---|---|---|
| currentBase64 | Yes | ||
| baselineBase64 | Yes |
Changes observed during successful MCP inspections.
v1.1.4Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. The word 'Compare' and the phrase 'return diff percentage' imply a non-mutating, computation-only operation, but the description does not address constraints such as equal image dimensions, invalid base64 handling, or whether the operation is side-effect-free in an explicit way.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence with no filler: it front-loads the action and resource, then states the output. Every clause contributes information, making it easy for an agent to parse quickly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a low-complexity tool with two required parameters and no output schema, the description covers the input domain and the output concept. However, it omits the exact return shape, the definition of 'diff percentage' (e.g., percentage of differing pixels), and constraints such as matching image dimensions. It is adequate but leaves room for ambiguity.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 0% schema description coverage, the schema only provides parameter names and string types, so the description must add meaning. It does this by identifying both parameters as base64-encoded images and clarifying their role in a comparison. It does not specify raw base64 versus data-URI format or size limits, but the core meaning of both parameters is clear.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description names a specific verb ('Compare'), precise inputs ('two base64 images'), and the result ('return diff percentage'). It is clearly distinct from the sibling device-control and image-capture tools, so an agent can immediately identify its purpose.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives, and it does not mention any exclusions or prerequisites. An agent cannot tell from the description whether this is the right choice for visual regression checking or how it relates to tools like get_element_image.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.