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Server Quality Checklist

67%
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  • Latest release: v0.1.0

  • Disambiguation5/5

    capture is solely concerned with layout overflow across viewport widths, while diagnose handles console errors, exceptions, and failed requests. Their purposes are clearly separated with no meaningful overlap.

    Naming Consistency4/5

    Both tool names are lowercase imperative verbs and follow the same two-tool pattern, but they are generic single words rather than descriptive verb_noun pairs. This is still internally consistent, though slightly less informative.

    Tool Count4/5

    Two tools is small, but the server appears intentionally scoped to frontend issue detection rather than broad browser automation. The count is slightly thin yet reasonable for the stated purpose.

    Completeness4/5

    The pair covers common frontend failure modes: visual overflow and runtime/network errors. Missing areas like accessibility or performance checks are not obviously required by the server's narrow focus, though the surface is minimal.

  • Average 4.2/5 across 2 of 2 tools scored.

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

    • No community issues in the last 6 months
    • 4 commits in the last 12 weeks
    • No stable releases found
    • No critical vulnerability alerts
    • No high-severity vulnerability alerts
    • No code scanning findings
    • CI is passing
  • 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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    {
      "$schema": "https://glama.ai/mcp/schemas/server.json",
      "maintainers": [
        "your-github-username"
      ]
    }

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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

  • Behavior4/5

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

    With no annotations provided, the description carries the behavioral burden. It discloses a key non-obvious trait: listeners attach before navigation, so load-time errors are captured. This adds meaningful context beyond the schema, though it does not detail edge cases like timeouts or unreachable pages.

    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 sentences with no filler. The primary action and outputs are front-loaded, and the second sentence adds the crucial listener-timing detail. Every word earns its place.

    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 simple two-parameter diagnostic tool, the description covers the main invocation need and return content. It lacks explicit output-format details or failure behavior, and there is no output schema to compensate, but the core usage is adequately specified.

    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 clear descriptions for 'url' and 'viewport'. The tool description does not add parameter-level details beyond what the schema already provides, so the baseline score of 3 is appropriate.

    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: load a page and report console errors, uncaught exceptions, and failed requests. This clearly differentiates it from the sibling tool 'capture', which implies visual capture rather than diagnostic reporting.

    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?

    Usage context is implied by the title and description: use this tool when you need to detect page-load errors, exceptions, or failed requests. However, there is no explicit guidance about when not to use it or how it relates to the sibling 'capture' tool.

    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?

    With no annotations provided, the description carries the disclosure burden and does well: it explains the tool loads pages, checks multiple viewports, reports overflow, returns text, and warns that screenshots are costly. It does not mention default viewport behavior or exact output formatting, but it discloses the most relevant operational traits.

    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 two concise sentences. The first sentence front-loads the core purpose and expected output, and the second sentence delivers cost-aware guidance for a specific parameter. Every clause adds useful information without redundancy.

    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 three parameters and no output schema, the description covers the main behavior, return type, and parameter nuance. It leaves a minor gap by not specifying what happens when the optional viewports parameter is omitted, but an agent can still use the tool effectively with the provided information.

    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 documents all three parameters at 100% coverage, so the baseline is 3. The description adds valuable meaning on top by warning that screenshots are expensive and should only be requested when genuinely needed, which helps agents avoid unnecessary resource use. The url and viewports parameters do not need additional explanation because the schema already covers them.

    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 action: load a page at multiple viewport widths, check for sideways overflow, and name the single responsible element. It clearly distinguishes this tool from a generic 'capture' by explaining the analysis it performs and the text result it returns.

    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 gives clear context for when to use the tool and practical guidance for the screenshots parameter, saying to pass screenshots:true only when an image is genuinely needed because each image is expensive. It does not explicitly compare to the sibling tool 'diagnose', but the intended use case is well implied.

    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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