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jbuchan

Accessibility MCP Server

by jbuchan
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Server Quality Checklist

58%
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  • Latest release: v1.0.0

  • Disambiguation5/5

    Each tool has a clearly distinct purpose: get_test_results retrieves specific results, list_test_results lists available files, and test_accessibility runs new tests. There is no overlap or ambiguity between these three functions.

    Naming Consistency5/5

    All tools follow a consistent verb_noun pattern (get_test_results, list_test_results, test_accessibility) with clear, descriptive names that align with their actions. There are no deviations in naming conventions.

    Tool Count4/5

    With 3 tools, the server is well-scoped for its accessibility testing domain, covering core operations: listing, retrieving, and running tests. It might benefit from additional tools like updating or deleting results, but the count is reasonable and not excessive.

    Completeness4/5

    The tools provide a solid foundation for accessibility testing, covering listing, retrieval, and execution. Minor gaps exist, such as the inability to update or delete test results, but agents can work around this, and the core workflow is adequately supported.

  • Average 3.1/5 across 3 of 3 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

  • Behavior2/5

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

    No annotations are provided, so the description carries full burden. It states retrieval but doesn't disclose behavioral traits like whether this is a read-only operation, if it requires specific permissions, what happens if the file doesn't exist (e.g., error handling), or if there are rate limits. The description is minimal and lacks operational context.

    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 with zero waste. It's front-loaded with the core action and resource, making it easy to parse quickly.

    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 no annotations and no output schema, the description is incomplete for a tool that retrieves data. It doesn't explain what the returned test results contain (e.g., format, structure), error conditions, or dependencies on other tools. For a retrieval operation with zero structured context, this leaves significant gaps.

    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%, with the single parameter 'fileName' well-documented in the schema. The description adds no additional parameter semantics beyond implying retrieval is filename-based, which the schema already covers. Baseline 3 is appropriate when schema does 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 verb ('Retrieve') and resource ('saved accessibility test results'), and specifies retrieval is 'by filename'. However, it doesn't explicitly differentiate from sibling tools like 'list_test_results' (which might list all results) or 'test_accessibility' (which might run new tests).

    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?

    No guidance is provided on when to use this tool versus alternatives. The description doesn't mention prerequisites (e.g., needing a saved file first), exclusions, or comparisons to siblings like 'list_test_results' for browsing or 'test_accessibility' for creating new results.

    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?

    With no annotations provided, the description carries the full burden of behavioral disclosure. It states the action ('List all available') but does not explain key traits such as whether this is a read-only operation, if it requires authentication, how results are formatted (e.g., pagination), or any rate limits. This leaves significant gaps in understanding the tool's behavior.

    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 with no wasted words. It is front-loaded with the core action and resource, making it highly efficient and easy to parse for an AI agent.

    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 lack of annotations and output schema, the description is incomplete for a tool that likely returns a list of files. It does not specify the format of the results (e.g., file names, metadata), any filtering criteria, or error handling. For a list operation, more context on the output is needed to be fully helpful.

    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 tool has zero parameters, and the input schema has 100% description coverage (though empty). The description does not need to add parameter details, as there are none to document. It appropriately avoids redundancy, earning a high score for this dimension.

    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 verb ('List') and resource ('accessibility test result files'), making the purpose explicit. However, it does not differentiate from sibling tools like 'get_test_results' or 'test_accessibility', which might have overlapping or distinct functionalities, preventing a perfect score.

    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 like 'get_test_results' or 'test_accessibility'. It lacks explicit instructions on context, prerequisites, or exclusions, leaving the agent with minimal usage direction.

    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?

    With no annotations provided, the description carries full burden for behavioral disclosure. While it mentions the testing framework and standards, it doesn't describe what the tool actually returns (e.g., report format, error handling), performance characteristics, authentication needs, or potential side effects like network usage or resource consumption.

    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 front-loads the core purpose without unnecessary elaboration. Every word contributes to understanding the tool's function, making it appropriately sized and well-structured.

    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?

    For a tool with 5 parameters, no annotations, and no output schema, the description is insufficient. It doesn't explain what the tool returns (critical for a testing tool), error conditions, or behavioral details beyond the basic action. The context signals indicate significant gaps that the description doesn't address.

    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 5 parameters thoroughly. The description adds no additional parameter semantics beyond what's in the schema, maintaining the baseline score of 3 for adequate but not enhanced parameter documentation.

    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 specific action ('Run accessibility tests'), target resource ('on a website'), and implementation details ('using Playwright and axe-core against WCAG standards'). It distinguishes itself from sibling tools like 'get_test_results' and 'list_test_results' by focusing on the testing action rather than retrieving results.

    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 siblings ('get_test_results', 'list_test_results'), nor does it mention any prerequisites, constraints, or alternative scenarios. It simply states what the tool does without contextual usage information.

    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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