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by ai.ravenmcp

audit_video_playback

Read-onlyIdempotent

Render a page in headless Chromium and observe whether each actually advances (samples currentTime before/after a play attempt), classifying every clip into playing|paused|stalled|empty|error with a reason. Catches black/non-playing videos that static audits miss — the most common real-world defect on marketing sites with video backgrounds. Pass url to render + observe, or dom_snapshot to classify pre-collected observations without a browser.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
urlNoURL to render and observe (http/https or file://). Requires headless chromium.
observeMsNoMilliseconds to wait between currentTime samples after play() attempt. Default: 1000
dom_snapshotNoPre-collected video observations to classify without rendering (deterministic path)

TDQS

A4.6/5.0
Behavior5/5

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

The description goes beyond the readOnly/idempotent annotations by disclosing the actual mechanism: headless Chromium rendering, play attempts, currentTime sampling, and classification with reasons. It also clarifies that the dom_snapshot path avoids a browser entirely, which is meaningful behavioral context that the annotations do not provide.

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?

Three sentences cover the mechanism, the value proposition, and the usage modes with no wasted words. The front-loaded sentence immediately explains the tool's behavior and classification output, making it easy to scan.

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?

The description is complete enough for an agent to correctly choose and aim this tool: it explains what happens, what it catches, and which parameter path to use. Its exact return shape is not described in detail, but the classification categories are named, and with no output schema, the coverage of behavior and parameters is strong enough to support a solid call.

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?

The input schema already provides 100% parameter descriptions, giving a baseline of 3. The description adds mode-level semantics beyond the schema: url means render and observe, while dom_snapshot means classify pre-collected observations without rendering. This improves the agent's understanding of how parameters relate to different execution paths.

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 states a specific verb and resource: audit video playback by rendering a page and observing whether videos advance. It clearly distinguishes itself from static audit siblings by focusing on actual currentTime movement and classifying clips into playing|paused|stalled|empty|error.

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 strong context by noting this tool catches black/non-playing videos that static audits miss, and it provides clear routing between the two operating modes: url for browser-based rendering or dom_snapshot for deterministic classification without a browser. It does not name sibling tools explicitly or state when not to use it, but the use context is clear.

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