DS4AI MCP

generate_image_markup

Generate responsive image markup with reserved dimensions, lazy/eager loading, LCP priority, and WebP/AVIF format negotiation for elements.

Instructions

Generate responsive or markup with reserved dimensions and correct scheduling. Mark the LCP image priority (eager, high fetchpriority); mark offscreen images lazy. Pass avif/webp for format negotiation to get a .

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "image": "<image>"
    },
    "name": "generate_image_markup"
  }
}

Input Schema

Name	Required	Description	Default
`image`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Aug 21, 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 full burden. It discloses key behaviors: reserving dimensions, setting loading=lazy for offscreen images, using eager+high fetchpriority for LCP, and generating <picture> when avif/webp are provided. It does not mention handling of srcset or sizes, but the core behavior is clearly articulated.

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?

The description is two sentences, front-loaded with the core purpose and followed by specific behavioral notes. Every sentence contributes value, with no filler or redundancy.

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?

For a tool with a nested object schema and no output schema, the description covers the main purpose, behavioral details, and key parameters. It doesn't elaborate on srcset/sizes behavior or output format, but as a markup generator, the output is implicitly the HTML string. The description is adequate for a capable agent, though leaving a few details to standard HTML knowledge.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, so the description must add meaning. It explains the role of avif/webp (trigger <picture>), priority (LCP eager), lazy (offscreen), and dimensions (reserved). While srcset, sizes, and alt are not explicitly explained, their names are self-explanatory in web context, and the description covers the most critical parameters.

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?

The description explicitly states the tool generates responsive <img> or <picture> markup, with a specific verb (Generate) and resource (markup). It clearly distinguishes from sibling tools like audit_surface or generate_sitemap by focusing on image markup generation and optimization.

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?

The description provides clear usage context: it instructs to mark LCP images with priority and offscreen images as lazy, and explains that passing avif/webp yields <picture>. While it doesn't explicitly exclude alternatives, there is no overlapping sibling tool, and these instructions give actionable when-to-use guidance.

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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