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Server Quality Checklist

67%
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  • Latest release: v0.1.0

  • Disambiguation5/5

    Each tool has a clearly distinct purpose: color contrast checking, retrieving patterns, listing patterns, and reviewing code. No overlap in functionality.

    Naming Consistency5/5

    All tool names follow a consistent verb_noun pattern (check_contrast, get_pattern, list_patterns, review_code), making the API predictable.

    Tool Count5/5

    With 4 tools, the server is well-scoped for its niche. It covers the core needs of an accessibility expert without being too sparse or bloated.

    Completeness4/5

    The server covers contrast checking, pattern reference, and code review. It lacks a dedicated tool for keyboard navigation testing or automated audit, but review_code can handle many issues.

  • Average 4.1/5 across 4 of 4 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.

  • No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.

    Tip: use the "Try in Browser" feature on the server page to seed initial usage.

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  • If you are the author, simply .

    If the server belongs to an organization, first add glama.json to the root of your repository:

    {
      "$schema": "https://glama.ai/mcp/schemas/server.json",
      "maintainers": [
        "your-github-username"
      ]
    }

    Then . Browse examples.

  • Add related servers to improve discoverability.

How to sync the server with GitHub?

Servers are automatically synced at least once per day, but you can also sync manually at any time to instantly update the server profile.

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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 the full burden. It doesn't disclose whether the operation is read-only, requires authentication, or has side effects. The return content is described, but behavioral traits like nondestructive nature are omitted.

    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 succinct and well-structured with 'Args:' and 'Returns:' sections. Every sentence adds value, and the main purpose is stated upfront without unnecessary words.

    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?

    For a tool with one parameter, the description covers purpose, input examples, and return details. It lacks information on error conditions or prerequisites, but the output description is rich. Overall, it provides a complete picture for a simple retrieval tool.

    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 0%, so the description must explain the parameter. It lists many example values and explains that the component name is expected. This adds significant meaning beyond the bare schema definition, though it doesn't specify constraints or error handling.

    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: 'Get the accessible implementation pattern for a UI component.' The verb 'get' and resource 'implementation pattern' are specific, and it distinguishes from siblings like 'list_patterns' which likely handles listing patterns.

    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 clarifies that the tool is used to fetch a detailed pattern for a given component, but it doesn't provide explicit when-not-to-use guidance or compare with sibling tools. It assumes the user knows to call it when they need specific ARIA practices, missing exclusions.

    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?

    No annotations are provided, so the description carries full burden. It indicates a read-only operation that returns a list, but lacks details on potential pagination, rate limits, or access control. The description is adequate but minimal.

    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 concise, with two sentences that directly state the tool's purpose and return value. Every sentence is essential and there is no redundancy.

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

    Completeness5/5

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

    For a simple list tool with no parameters and an output schema (not shown), the description sufficiently covers its purpose and return format. No additional details are necessary given the tool's straightforward nature.

    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 no parameters, and schema coverage is trivially 100%. The description appropriately adds no extra parameter information, which is acceptable for zero-parameter tools (baseline 4).

    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 lists all available accessible component patterns, with a specific verb 'list' and resource 'component patterns'. It distinguishes from siblings like get_pattern (which likely retrieves a single pattern) and review_code (different 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 alternatives like get_pattern or check_contrast. Given sibling tools exist, this lack of context may lead to incorrect tool selection.

    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?

    Partially discloses behavior by describing return values (ratio and pass/fail), but does not cover error handling, side effects, or authentication needs. Without annotations, more detail would be beneficial.

    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?

    Two concise paragraphs covering purpose, arguments, and returns. No unnecessary words, well-structured for quick scanning.

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

    Completeness5/5

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

    Complete for a simple two-parameter tool with output schema: describes return content, no missing context. Handles the task fully.

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

    Parameters5/5

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

    Adds significant meaning beyond the schema: explains hex format with examples ('#333333' or '333'), distinguishes foreground and background roles. With 0% schema coverage, description fully compensates.

    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?

    Clearly states the tool checks if two colors meet WCAG contrast ratio requirements, specifying use of hex colors and pass/fail for AA and AAA. Distinguishes from siblings like get_pattern and list_patterns which serve different purposes.

    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?

    Implies usage for accessibility contrast checking but lacks explicit guidance on when to use this tool versus alternatives, or any conditions where it should not be used. No exclusions or context provided.

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

  • Behavior4/5

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

    No annotations provided, so description carries burden. It discloses the tool finds issues, suggests fixes, and references WAI-ARIA patterns. Lacks details on output format or edge cases, but adequate for an analysis tool.

    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?

    Two-line purpose followed by concise arg/return docs. Every sentence adds value, no wasted words. Front-loaded with main action.

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

    Completeness5/5

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

    Given the tool's simplicity (two params, returns issues), the description covers inputs and outputs sufficiently. With an output schema presumably available, full completeness is achieved.

    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 coverage is 0%, so description must compensate. It adds context for 'code' (acceptable formats) and 'component_type' (examples like modal, dropdown), improving understanding beyond type alone.

    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?

    Clearly states 'Review code for accessibility issues and suggest fixes,' with a specific verb and resource. Differentiates from siblings (check_contrast, get_pattern, list_patterns) which focus on contrast or pattern retrieval.

    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?

    Describes the context for use (reviewing code for accessibility) but does not explicitly state when not to use or suggest alternatives. Clear enough for an agent to infer usage.

    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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  • Confirm that there are no obvious security issues.
  • Evaluate tool definition quality.

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