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noGarne

lighthouse-mcp

by noGarne
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JavaScript
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Server Quality Checklist

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

  • Latest release: v1.0.0

  • Disambiguation5/5

    The two tools are clearly distinct: audit_url handles a single URL while audit_batch handles multiple URLs. There is no overlap or ambiguity in their purposes.

    Naming Consistency5/5

    Both tools use the consistent verb_noun pattern (audit_url, audit_batch), making the naming predictable and easy to follow.

    Tool Count3/5

    With only 2 tools, the server feels thin for a full-featured Lighthouse server, though the tools cover the core audit functionality. It is below the typical 3-15 range.

    Completeness4/5

    The tools cover both single and batch audits, returning comprehensive metrics and opportunities. Minor gaps include lack of custom configuration options (e.g., device, categories) or historical comparison, but these are not critical.

  • Average 4/5 across 2 of 2 tools scored.

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

    • No community issues in the last 6 months
    • 1 commit 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

  • Behavior3/5

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

    With no annotations provided, the description carries the full transparency burden. It discloses parallel execution and the returned summary/detailed metrics, but does not mention potential side effects, time/resource costs, or concurrency limits beyond what the schema implies.

    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 sentences, gets straight to the point, and uses no filler. Every phrase adds value: what the tool runs, the parallel nature, and what it returns.

    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 tool with two well-documented parameters and no output schema, the description covers the high-level return behavior (summary plus detailed metrics). It does not mention the device parameter default, but that is already in the schema, so this is reasonably complete.

    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 coverage is 100% and both parameters already have clear descriptions in the schema. The description adds context about Core Web Vitals, but does not enrich parameter meaning further, so the baseline 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 uses a specific verb ('Run') and clearly identifies the resource ('Lighthouse performance audits') and scope ('multiple URLs in parallel'). It distinguishes itself from the sibling tool audit_url by emphasizing batch execution and comparative Core Web Vitals results.

    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 phrase 'multiple URLs in parallel' clearly implies a batch use case and contrasts with a single-URL tool like audit_url. However, it does not explicitly state when not to use this tool or name audit_url as an alternative for single-URL audits.

    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 the full burden. It describes outputs in detail but does not disclose any side effects, prerequisites (e.g., URL accessibility), rate limits, or operational characteristics like execution time. The read-only nature of an audit is implied but not explicit.

    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 dense sentence that lists concrete return values without repetition or filler. It is front-loaded with the action ('Run a Lighthouse performance audit') and efficiently covers key details.

    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?

    Given the absence of an output schema and annotations, the description sufficiently enumerates return values (Core Web Vitals, metrics, category scores, improvement opportunities). It does not cover error cases, limitations, or output format, but for a single-URL audit tool this is largely adequate.

    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 covers 100% of parameters with descriptions, so the baseline is 3. The description adds no new meaning to the device or categories parameters; it only mentions metrics and scores, which relate to output rather than parameter selection.

    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 it runs a Lighthouse performance audit on a URL and enumerates the specific metrics returned (Core Web Vitals, performance metrics, category scores, improvement opportunities). It distinguishes itself from the sibling audit_batch by emphasizing a single URL.

    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 phrase 'on a URL' gives clear context that this tool is for auditing a single URL, implicitly contrasting with audit_batch. However, it does not explicitly mention when to use audit_batch instead or provide exclusions.

    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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