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fullstackdegen

lighthouse-mcp

by fullstackdegen
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Server Quality Checklist

67%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v0.3.1

  • Disambiguation5/5

    Only one tool exists, so there is no risk of confusion between tools. The agent will always select the correct one.

    Naming Consistency5/5

    The single tool name follows a clear verb_noun pattern and is self-consistent. There are no other tools to conflict with.

    Tool Count3/5

    With only one tool, the server feels thin for a domain like website performance analysis, which could benefit from multiple tools (e.g., scheduling, comparing). However, for a focused use case it is acceptable.

    Completeness4/5

    The single tool covers the core functionality of running Lighthouse audits and generating reports. Minor gaps might include the ability to run only specific audits or retrieve historical reports, but these are not severe.

  • Average 3.7/5 across 1 of 1 tools scored.

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

    • No community issues in the last 6 months
    • 61 commits in the last 12 weeks
    • Last stable release on
    • 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.

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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 must disclose all behavioral traits. It mentions 'reliable' and 'fast' modes but does not explain side effects, auth needs, rate limits, or potential resource consumption. The description is insufficient for full behavioral understanding.

    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 a single sentence that front-loads the key action and output. It is concise and efficient, though it could be structured as more than one sentence for clarity. Minimal waste.

    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?

    Output schema exists, so return values are covered. The description mentions 'implementation-ready structured report with equivalent Markdown,' giving a clear picture of the output. No sibling tools, so additional context is not needed. The description is largely complete for its purpose.

    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 100%, baseline 3. The description adds value by stating the tool runs audits for both mobile and desktop, which is not in the schema. This extra context improves parameter understanding beyond what the schema provides.

    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 runs Lighthouse audits for both mobile and desktop, and produces a structured report with Markdown. It specifies the verb (runs), resource (Lighthouse audits), and output format, making the purpose unambiguous.

    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?

    No explicit guidance on when to use this tool vs alternatives, nor prerequisites or limitations. The description implies the 'reliable' mode is default but does not explain when 'fast' is appropriate. With no sibling tools, the lack of explicit when-not-to-use is a moderate gap.

    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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