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PashaBoiko

Playwright Accessibility Testing MCP Server

by PashaBoiko
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Server Quality Checklist

58%
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  • Latest release: v1.0.0

  • Disambiguation4/5

    The two tools have clearly distinct purposes: a11y_scanInteractiveByText focuses on testing components via visible text/labels for natural language scenarios, while a11y_scanUrl handles URL-based or element-specific scanning. There is minor potential for confusion since both perform accessibility scans, but their different input methods (text vs. URL/element) and use cases (interactive components vs. general pages) make them mostly distinguishable.

    Naming Consistency5/5

    Both tools follow a consistent naming pattern with the prefix 'a11y_scan' followed by descriptive suffixes (InteractiveByText and Url). They use snake_case uniformly, and the names clearly indicate their distinct functionalities, making them predictable and easy to understand.

    Tool Count3/5

    With only two tools, the server feels thin for an accessibility testing domain, which might involve more operations like generating reports, filtering results, or testing specific WCAG criteria. However, the tools cover core scanning functionalities, so it's borderline but not severely lacking.

    Completeness3/5

    The server provides basic scanning capabilities for URLs and interactive components, but there are notable gaps. Missing operations include result analysis tools (e.g., summarize findings, export reports), configuration options (e.g., custom rule sets), and follow-up actions (e.g., retest after fixes). This limits agents to scanning without deeper workflow support.

  • Average 3.9/5 across 2 of 2 tools scored.

    See the Tool Scores section below for per-tool breakdowns.

    • No community issues in the last 6 months
    • 0 commits in the last 12 weeks
    • No stable releases found
    • No critical vulnerability alerts
    • No high-severity vulnerability alerts
    • No code scanning findings
    • CI status not available

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How is the quality score calculated?

The overall quality score combines two components: Tool Definition Quality (70%) and Server Coherence (30%).

Tool Definition Quality measures how well each tool describes itself to AI agents. Every tool is scored 1–5 across six dimensions: Purpose Clarity (25%), Usage Guidelines (20%), Behavioral Transparency (20%), Parameter Semantics (15%), Conciseness & Structure (10%), and Contextual Completeness (10%). The server-level definition quality score is calculated as 60% mean TDQS + 40% minimum TDQS, so a single poorly described tool pulls the score down.

Server Coherence evaluates how well the tools work together as a set, scoring four dimensions equally: Disambiguation (can agents tell tools apart?), Naming Consistency, Tool Count Appropriateness, and Completeness (are there gaps in the tool surface?).

Tiers are derived from the overall score: A (≥3.5), B (≥3.0), C (≥2.0), D (≥1.0), F (<1.0). B and above is considered passing.

Tool Scores

  • Behavior3/5

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

    No annotations are provided, so the description carries the full burden. It discloses some behavioral traits: it uses 'comprehensive WCAG 2.0, 2.1 Level A/AA and best-practice rules by default,' which indicates the scanning standards. However, it does not mention performance aspects (e.g., rate limits), authentication needs, or what happens if scanning fails. The description adds context but does not fully compensate for the lack of 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?

    The description is appropriately sized with two sentences that efficiently convey key information: the core functionality and the scanning standards. It is front-loaded with the main purpose. However, the second sentence could be slightly more concise, and there is minor redundancy (e.g., 'comprehensive' might be implied).

    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?

    Given the complexity (accessibility scanning tool with 5 parameters), no annotations, and no output schema, the description is moderately complete. It covers the purpose, technology, and standards but lacks details on output format, error handling, or integration context. It should do more to compensate for the missing structured data, especially for a tool with multiple parameters and no output schema.

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

    Parameters3/5

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

    Schema description coverage is 100%, so the schema already documents all parameters thoroughly. The description adds minimal value beyond the schema by mentioning 'optional screenshots' (related to captureScreenshot) and 'supports full page or targeted section scanning' (related to selector), but it does not provide additional syntax, format, or usage details. Baseline 3 is appropriate as the schema does the heavy lifting.

    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 tool's purpose: 'Run accessibility scan on a URL or specific element using Playwright + axe-core.' It specifies the verb ('scan'), resource ('URL or specific element'), technology stack ('Playwright + axe-core'), and scope ('full page or targeted section'). It distinguishes from the sibling tool 'a11y_scanInteractiveByText' by focusing on URL-based scanning rather than interactive text-based scanning.

    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 implies usage context by mentioning 'full page or targeted section scanning' and 'optional screenshots,' but it does not explicitly state when to use this tool versus the sibling 'a11y_scanInteractiveByText' or other alternatives. It provides some guidance on capabilities (e.g., supports dynamic content via waitForSelector) but lacks explicit when/when-not instructions or named alternatives.

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

  • 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 of behavioral disclosure. It describes key behaviors: 'Automatically discovers and tests interactive elements' and 'Uses comprehensive WCAG 2.0, 2.1 Level A/AA and best-practice rules by default.' However, it lacks details on permissions, rate limits, error handling, or what the test results look like (especially since there's no output schema).

    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 front-loaded with the main purpose, followed by usage examples and default rules. Every sentence adds value: the first defines the tool, the second explains automation, the third gives usage context, and the fourth specifies standards. No wasted words.

    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?

    Given the tool's complexity (6 parameters, no annotations, no output schema), the description is incomplete. It covers the 'what' and 'when' well but lacks details on behavioral traits (e.g., what happens during testing, error scenarios) and output format. Without an output schema, the description should ideally hint at return values.

    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 67%, and the description adds meaningful context beyond the schema. It explains the core concept: 'finding them using visible text/labels' and 'automatically discovers and tests interactive elements,' which clarifies the purpose of containerText and autoDiscover parameters. However, it doesn't detail all six parameters (e.g., customInteractions, captureScreenshots).

    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 tool's purpose: 'Test accessibility of components by finding them using visible text/labels instead of CSS selectors. Automatically discovers and tests interactive elements.' It specifies the verb ('test accessibility'), resource ('components'), and method ('by finding them using visible text/labels'), and distinguishes it from sibling a11y_scanUrl by emphasizing text-based discovery rather than URL-only scanning.

    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?

    The description provides clear context for when to use this tool: 'Perfect for natural language testing like 'test the Rewards section' or 'test the user menu'.' It implies usage for testing specific UI sections identified by text, but does not explicitly state when not to use it or name alternatives (e.g., when to use a11y_scanUrl instead).

    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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