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Raven

by ai.ravenmcp

audit_contrast

Read-onlyIdempotent

Compute WCAG contrast ratios for every text element on a rendered page (pass url) or from a supplied dom_snapshot. Reports AA (4.5:1 normal, 3:1 large) and AAA pass/fail and surfaces failing pairs with selector, ratio, and delta-to-pass — replacing manual eyedropper + ratio math. dom_snapshot mode returns a row per supplied element; url mode returns counts plus every row that needs action (aa_failures, indeterminate_bg_rows) and OMITS the per-element rows for passing elements, because a real page produces hundreds of thousands of characters of them.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
urlNoURL to render and measure (http/https or file://)
screenshotNoOptional base64 PNG for caller reference; ratios are computed from the DOM, not pixels
dom_snapshotNoPre-collected text elements to score without rendering

TDQS

A4.6/5.0
Behavior5/5

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

Read-only annotations already tell the agent this is non-destructive, but the description adds genuinely important behavior: url mode omits passing per-element rows, returns counts plus aa_failures/indeterminate_bg_rows, and explains why to prevent misinterpretation. This is exactly the kind of output-trait disclosure agents need beyond annotations.

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?

It is dense but appropriately sized for a tool with two modes and complex output omission behavior. The core purpose and input choice are front-loaded, and the rationale for omitting passing elements is not wasted, though the middle sentence is long and mixes a pitch with technical spec.

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

Completeness5/5

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

Given there is no output schema, the description carries the full burden of explaining return behavior, and it does so thoroughly: thresholds, actionable rows, indeterminate-bg rows, mode-specific output, and intentional omission. An agent can form accurate expectations about both the result size and correctness of interpretation.

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 coverage is 100%, and the property descriptions already explain url, screenshot, and dom_snapshot. The main description adds one crucial cross-property meaning: url and dom_snapshot are alternative input modes, and each mode implies different output semantics, which is not expressed in the schema alone.

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 opens with a specific action and resource: compute WCAG contrast ratios for all text elements, using either a rendered page or a supplied dom_snapshot. It clearly distinguishes this from the many audit_* siblings by focusing on contrast measurement and reporting pass/fail plus actionable ratios.

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?

It gives clear context for both usage modes: pass a url to audit a real page or pass a dom_snapshot when elements are pre-collected. It does not explicitly state which sibling tools to use instead in other cases, so it stops just short of full exclusion 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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TDQS

A3.7/5.0
Disambiguation4/5

Most tools have clearly distinct purposes (audit_* vs get_* vs list_* vs generate_* vs score_*), but there is notable overlap among audit_page, audit_layout, score_page, and audit_url (all audit rendered HTML, with audit_page and score_page explicitly sharing checks; audit_screen and audit_ios_screen are aliases). The get_* family (get_pattern vs get_content_pattern vs get_service_pattern, get_principles vs get_brand_principles vs get_content_principles) have overlapping boundaries that may cause misselection.

Naming Consistency4/5

Names follow a consistent verb_noun pattern (audit_*, get_*, list_*, generate_*, score_*, compose_*, suggest_*, search_*), which is predictable and readable. Minor deviations exist: 'evaluate_design' uses evaluate_ instead of audit_/score_, and 'process' isn't present but 'compose_system' uses compose_ instead of generate_/get_. Overall the convention is strong and consistent.

Tool Count2/5

45 tools is far beyond the typical well-scoped server (3-15 tools) and even beyond the 'heavy' 25+ threshold. The server appears to be an all-in-one design/UX knowledge base and auditing suite, but the sheer count makes discovery and selection overwhelming, and many tools (e.g., multiple audit_* variants for mobile platforms) could be consolidated.

Completeness3/5

The server covers a wide domain: audits for web/mobile/RN/SwiftUI, design tokens, UX principles, content systems, business strategy, creative scoring, and service design. However, there are gaps: no tool for creating or editing design systems (only get/generate), no update/delete operations anywhere (all read-only or audit-only), and the creative side has list/score but no generation tool. The set feels broad but shallow in lifecycle coverage.

Resources