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zFinia Intelligence

Accessibility Contrast Calculator

accessibility_contrast_calculator

Calculate WCAG 2.x sRGB contrast for one caller-supplied colour pair; no interface-wide accessibility claim is made.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
backgroundYes
foregroundYes
large_textYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
wcag_aaYes
wcag_aaaYes
large_textYes
limitationsYes
contrast_ratioYes

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observed

TDQS

B3.3/5.0
Behavior3/5

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

With no annotations provided, the description carries the full burden. It discloses that the tool computes WCAG 2.x sRGB contrast and explicitly states it makes no interface-wide claim, which is a scope disclaimer. However, it does not describe error behavior, handling of invalid inputs (though schema enforces pattern), or what the output represents (e.g., ratio vs pass/fail). The addition of the WCAG standard and scope disclaimer adds some value beyond the name, but significant behavioral details remain undisclosed.

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 a single sentence that front-loads the verb and resource, then adds the scope disclaimer. Every word earns its place—there is no fluff, and the key differentiator (single pair vs interface-wide) is included. It is optimally concise.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Although an output schema exists (so return format is covered), the description fails to explain the purpose of the large_text parameter or clarify what the computed contrast value represents. An agent might infer a ratio, but the boolean's role is ambiguous. For a tool with only three parameters, this omission is a meaningful gap.

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

Parameters2/5

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

Schema description coverage is 0%, so the description must compensate. It does not mention any parameters by name or explain their meaning. While foreground and background are self-explanatory hex colors, the boolean large_text is left unexplained; it likely affects WCAG thresholds, but the description says nothing about it. The description adds no value beyond what the schema pattern already provides for colors.

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 clearly states the specific action (calculate WCAG 2.x sRGB contrast) and the resource (a caller-supplied color pair). It also distinguishes itself from interface-wide accessibility claims, which separates it from potential broader audit tools. The verb and resource are explicit and unambiguous.

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

Usage Guidelines2/5

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

There is no explicit guidance on when to use this tool versus alternatives. The description implies it is for a single pair, but does not state when not to use it or point to a sibling for broader checks. Since siblings are unrelated, it's not a critical gap, but the lack of any contextual 'when to use' instruction lowers the score.

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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