Skip to main content
Glama

Raven

by ai.ravenmcp

audit_consistency

Read-onlyIdempotent

Audit multiple pages for cross-page consistency of content-container width and hero heading tier. Pass ≥2 pages ({name, html}) collected from different routes on the same site. Infers the canonical (modal) value from the corpus when no token is supplied, so you need not know the project's design token in advance. Flags the issue #9 single-blob blind spot: pages that each pass audit_page but silently disagree with each other on container width or hero size class. Returns per-page extraction (container_px, container_classes, hero_classes, signatures), consistency dimensions with reference values, outlier page names, issues[], score (100/50/0 → A/C/D), and a plain-text summary. Pure offline — no browser, no network.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
pagesYesAt least 2 pages to compare. Each entry is {name, html}.
hero_tokenNoCanonical hero heading class signature (e.g. "text-display-xl" or "64"). When supplied, hero divergence is measured against this token rather than the corpus modal.
container_tokenNoProject's canonical container width in px (e.g. 1152). When supplied, container divergence is measured against this token rather than the corpus modal.

TDQS

A4.6/5.0
Behavior5/5

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

Description adds behavioral context beyond annotations: 'pure offline, no browser/network'. It states corpus-modal inference which is not present in the annotations. No contradictions with annotations; annotations carry readOnlyHint, openWorldHint=false, idempotentHint=true, destructiveHint=false, but the description adds significant offline/no-network behavior and return summary detail.

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?

Information-dense single-paragraph description with semicolon-separated enumerations; each clause carries details (score semantics, per-page extraction, references). However, the density is a bit high and structuring a few bullets would aid; no wasteful sentences.

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?

No output schema, but description fully specifies return values: per-page extraction, consistency dimensions with references, issue list with outlier names, score formats, summary formats. It also specifies edge-case behavior (when token omitted and flags). This is detailed and complete for invocation.

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 100% and required parameters are fully described. Description adds a behavioral note about the inference of a default canonical value from a corpus when token is omitted. This is nuanced and does not directly explain each parameter in a way that is especially necessary, so the baseline is 3, lifted for the added inference context.

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?

Description has a specific verb ('Audit') + resource ('multiple pages for cross-page consistency') + scope ('content-container width and hero heading tier'), and distinguishes from sibling tools by referencing the 'issue #9 single-blob blind spot' and 'pages that each pass audit_page'. Highly specific and clear.

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?

Description gives explicit requirements (≥2 pages with {name, html}) and explicitly calls out the alternative usage context (corpus modal inference when no token supplied) but does not explicitly state when to use this tool vs siblings like audit_page or audit_contrast. Context is clear, exclusions are not.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

All Raven toolsInspect
Try in Browser
RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

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