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Raven

by ai.ravenmcp

get_principles

Read-onlyIdempotent

Get design principles relevant to a UI context. Returns usability heuristics, laws of UX, Gestalt principles, accessibility requirements, typography rules, and color theory — matched to what you're designing.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
formatNoOutput format: full (all details), checklist (implications + violations), brief (just summary). Default: full
contextYesWhat you're designing (e.g. 'signup form', 'pricing page', 'mobile nav', 'dark dashboard')
categoryNoFilter to category: accessibility, brand, color-systems, color-theory, component-architecture, d4d, gestalt, laws-of-ux, mobile-ux, nielsen-heuristics, research, responsive-layout, service-design, spacing-systems, typography, ux-writing
platformNoPlatform context. 'ios' returns Apple HIG principles (Dynamic Type, 44pt targets, SF Symbols, safe areas, dark-mode, haptics, App Review privacy); 'react-native' returns RN principles (44/48pt+hitSlop, accessibilityLabel/Role, font scaling, SafeAreaView, dark mode, iOS+Android parity, secrets). Both replace the web/CSS-oriented set. Default: web.

TDQS

A3.5/5.0
Behavior3/5

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

Annotations mark readOnly, idempotent, and non-destructive, matching the absence of side effects. However, the description does not explicitly state that no mutations occur, and it only adds minimal context, so a moderate score is warranted.

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?

Description is compact but information-dense with bullet-like structure: scoped intro, parameters inline, and line-separated fields. Clear front-loaded purpose. No redundant API details.

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

Completeness3/5

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

Four parameters (one required) with 100% documented schema and rich enum descriptions, but no output schema, no return-value description, and no elaboration of output format beyond the stated variants. Also no nested structures.

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?

Input schema covers all four parameters with rich descriptions for each: context ('what you're designing'), format ('full/checklist/brief'), category (filtering knowledge areas), and platform ('web/ios/react-native' with platform-specific behaviors). This goes beyond the schema's raw types/enums by giving design-context guidance.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description names a specific action and resource ('Get design principles relevant to a UI context') and enumerates concrete knowledge areas (usability heuristics, UX laws, Gestalt principles, accessibility, typography, color theory). It is distinct from sibling tools like get_design_system or get_brand_principles, but does not explicitly name alternatives or exclusions.

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

Usage Guidelines3/5

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

The description conveys 'for design decisions, grounded in design principles,' but does not explicitly state when not to use the tool or name alternatives like get_content_system, get_design_system, or audit_contrast. It provides clear context though.

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