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bilhasry-deriv

Web Accessibility MCP Server

by bilhasry-deriv
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Server Quality Checklist

58%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v1.0.0

  • Disambiguation5/5

    The two tools have clearly distinct purposes: one checks general web accessibility using axe-core, while the other specifically simulates colorblindness effects on a webpage. There is no overlap or ambiguity between them.

    Naming Consistency5/5

    Both tools follow a consistent verb_noun pattern (check_accessibility, simulate_colorblind) with clear, descriptive names. The naming style is uniform and predictable throughout the set.

    Tool Count2/5

    With only two tools, the server feels thin for a web accessibility domain. While the tools are useful, typical accessibility testing involves more operations like checking screen reader compatibility, keyboard navigation, or ARIA attributes, suggesting notable gaps in coverage.

    Completeness2/5

    The tool set is severely incomplete for web accessibility. It lacks core operations such as validating HTML structure, testing screen reader output, assessing keyboard accessibility, or generating accessibility reports, which are essential for comprehensive accessibility evaluation.

  • Average 2.9/5 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

  • This repository is licensed under MIT License.

  • This repository includes a README.md file.

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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

  • Behavior2/5

    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 states what the tool does but doesn't describe how it behaves: it doesn't mention whether this is a read-only analysis, what the output format might be, potential rate limits, authentication requirements, or error conditions. For a tool that performs web analysis, this leaves significant behavioral gaps.

    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 a single, efficient sentence that communicates the core purpose without any wasted words. It's appropriately sized for a tool with a clear, focused function and is front-loaded with the essential information.

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

    Completeness2/5

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

    Given that there are no annotations and no output schema, the description should provide more complete context for this accessibility checking tool. It doesn't explain what kind of results to expect, what accessibility standards are checked, whether the analysis is comprehensive or limited, or how the tool handles dynamic content. For a tool with 3 parameters and no structured output documentation, this is insufficient.

    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?

    The input schema has 100% description coverage, so all parameters are documented in the schema itself. The description doesn't add any parameter-specific information beyond what's already in the schema descriptions. According to the scoring rules, when schema_description_coverage is high (>80%), the baseline is 3 even with no param info in the description.

    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 clearly states the tool's purpose with a specific verb ('Check') and resource ('web accessibility of a given URL'), and mentions the technology used ('axe-core'). However, it doesn't explicitly differentiate from its sibling tool 'simulate_colorblind', which appears to be a related but distinct accessibility function.

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

    Usage Guidelines2/5

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

    The description provides no guidance on when to use this tool versus its sibling 'simulate_colorblind' or other alternatives. It doesn't mention prerequisites, typical use cases, or exclusions, leaving the agent with no contextual usage information beyond the basic purpose.

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

  • Behavior2/5

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

    No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool simulates colorblind views but doesn't describe how (e.g., generates a screenshot, modifies display, or returns data), what the output is (e.g., image file, visual report), or any behavioral traits like performance, rate limits, or side effects. This leaves significant gaps for an agent to understand the tool's operation.

    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 a single, clear sentence: 'Simulate how a webpage looks for colorblind users.' It is front-loaded with the core purpose, has zero wasted words, and is appropriately sized for the tool's complexity. Every part of the sentence earns its place by conveying essential information efficiently.

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

    Completeness2/5

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

    Given the tool's moderate complexity (4 parameters, no output schema, no annotations), the description is incomplete. It lacks details on output (e.g., what is returned or saved), behavioral context (e.g., how simulation works, any limitations), and usage guidelines. While the schema covers parameters well, the description doesn't compensate for missing annotations or output schema, leaving the agent with insufficient context for effective use.

    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?

    The input schema has 100% description coverage, clearly documenting all four parameters (url, type, outputPath, userAgent) with details like enum values for 'type.' The description doesn't add any parameter-specific information beyond what the schema provides, such as explaining the simulation process or output format. Given the high schema coverage, a baseline score of 3 is appropriate as the schema handles the heavy lifting.

    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 clearly states the tool's purpose: 'Simulate how a webpage looks for colorblind users.' It specifies the action (simulate) and resource (webpage appearance for colorblind users), making it easy to understand. However, it doesn't explicitly differentiate from its sibling tool 'check_accessibility,' which might also involve accessibility testing, though the focus here is specifically on colorblind simulation.

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

    Usage Guidelines2/5

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

    The description provides no guidance on when to use this tool versus alternatives. It doesn't mention the sibling tool 'check_accessibility' or any other tools, nor does it specify prerequisites, contexts, or exclusions. Usage is implied from the purpose but lacks explicit direction.

    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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