imagedimensions-mcp
Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@imagedimensions-mcpaudit images on https://example.com"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
imagedimensions-mcp
An MCP server that audits the images on any public web page — natural vs. rendered dimensions, oversized-image detection, and format breakdown — so AI agents (Claude, Cursor, Windsurf, etc.) can check image performance during development.
Powered by imagedimensions.com. The scan runs server-side, so no local browser or Chrome install is required.
Install / run
// Claude Desktop / any MCP client config
{
"mcpServers": {
"imagedimensions": {
"command": "npx",
"args": ["-y", "imagedimensions-mcp"]
}
}
}Related MCP server: accessibility-mcp-server
Tool
scan_image_dimensions
Param | Type | Description |
| string (required) | Public URL of the page to audit. |
| number (optional) | Area-overshoot ratio to flag as oversized. Default |
Returns a text report plus structured content:
total / visible image counts and CSS-background count
modern-format share (WebP + AVIF)
format breakdown
the list of oversized images (natural → rendered, overshoot ratio, format, src)
a link to the full visual report on imagedimensions.com
Example agent uses: "Audit the images on https://example.com," "Which images on this page are oversized and hurting LCP?", "What % of this site's images use modern formats?"
Why "oversized" matters
An image downloaded much larger than the box it renders into wastes bandwidth and slows Largest Contentful Paint — the most common image-performance mistake on the web. See the writeup.
Config
IMAGEDIMENSIONS_API_BASE— override the API base (defaulthttps://imagedimensions.com).
License
MIT
Available Tools
1 toolscan_image_dimensionsScan image dimensionsA
Audit the images on any public web page. Returns each image's natural vs rendered dimensions, flags oversized images (downloaded much larger than displayed — a common Largest Contentful Paint problem), and breaks down formats (WebP/AVIF adoption). Useful for web performance and image-optimization work.
| Name | Required | Description | Default |
|---|---|---|---|
| url | Yes | The public URL of the page to audit. | |
| oversizedThreshold | No | Area-overshoot ratio above which an image is flagged oversized. Default 4 (≈2x in each dimension). |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It discloses the main behaviors (returns dimensions, flags oversized images, breaks down formats) but lacks details on rate limits, error handling, or network requests. With no annotations, a score of 3 is appropriate—adequate but with gaps.
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 two sentences with no wasted words. The first sentence introduces the main action, and the second adds detail and use case. Every sentence is informative.
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?
No output schema exists, but the description explains the return values (natural vs rendered dimensions, flagged oversized images, format breakdown). It gives a clear picture of the output, though it could mention whether results are returned as a list or summary, or how errors are reported.
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?
Schema coverage is 100%, so parameters are well-documented in the schema. The description adds context by explaining the 'oversizedThreshold' parameter in relation to the Largest Contentful Paint problem, and reinforces that 'url' is for a public page. This adds value beyond the schema.
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 clearly states the tool audits images on a public web page, returning dimensions, flagging oversized images, and breaking down formats. The verb 'audit' combined with the specific resource 'images on any public web page' makes the purpose unambiguous.
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 explicitly states the tool is 'useful for web performance and image-optimization work,' providing clear context. While no alternatives or exclusions are mentioned, there are no sibling tools requiring differentiation, so the guidance is sufficient.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
1 tool update
v0.1.1- First observed
scan_image_dimensions
TDQS
Scored across 1 tool
Only one tool exists, so there is no possibility of confusion with other tools.
The single tool name is descriptive and follows a clear verb_noun pattern.
The server has exactly one tool for its focused purpose, which is appropriate for the narrow domain.
The tool fully covers its stated purpose: auditing image dimensions on public web pages with detailed output.
Maintenance
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