Skip to main content
Glama
jladev

MCP Lighthouse Audit

by jladev
OverviewSchemaRelated ServersScoreDiscussions
JavaScript
Local

Server Quality Checklist

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

  • Latest release: v1.0.0

  • Disambiguation5/5

    With only one tool, there is no possibility of confusion between tools. The purpose is clearly defined as running a Lighthouse audit.

    Naming Consistency5/5

    The single tool name 'audit_page' follows a clear verb_noun pattern, which is consistent and descriptive.

    Tool Count3/5

    A single tool for a Lighthouse audit server is functional but feels minimal. The tool is well-scoped, yet the server could benefit from additional related tools (e.g., audit_mobile, compare_audits).

    Completeness4/5

    The tool covers the core audit action comprehensively, returning all category scores and failed audits. However, there are no supplementary operations like retrieving historical audits or configuring audit options.

  • Average 3.5/5 across 1 of 1 tools scored.

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

    • No community issues in the last 6 months
    • 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.

  • Add a glama.json file to provide metadata about your server.

  • 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.

To manually sync the server, click the "Sync Server" button in the MCP server admin interface.

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?

    With no annotations, the description carries the full transparency burden. It mentions returning scores and failed audits but does not disclose typical execution time, resource intensity, potential side effects (even if none), or limitations like whether localhost is supported. As a read-only audit, it should ideally state that it is non-destructive or note rate limits, but it does not.

    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, well-structured sentence that front-loads the verb and resource. It is highly concise, providing no redundant information, and every word adds value.

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

    Completeness3/5

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

    The tool has no output schema, so the description's mention of returned scores and failed audits is valuable. However, it does not explain the output format or potential error conditions, and it omits any note about preset/category defaults. Given the tool's moderate complexity, the description is adequate but leaves some questions about behavior beyond the basics.

    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 provides 100% coverage of all parameters (url, preset, categories) with descriptions, so the tool description does not need to repeat them. The description adds a slight contextual hint by saying 'all categories,' but it does not enhance understanding of parameter syntax, defaults, or interactions beyond the schema. 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 clearly specifies the tool's action: 'Run a detailed Lighthouse audit on a URL.' It also discloses the primary return value (scores for all categories, list of failed audits), giving the agent a precise understanding of what the tool does. No sibling tools exist, so differentiation isn't needed, but the description would stand out on its own.

    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 implies usage for auditing URLs but does not provide explicit when-to-use guidance, prerequisites, or exclusions. Since there are no sibling tools, the absence of alternative references is acceptable, but it still lacks context like 'use this to evaluate page quality' or conditions such as URL accessibility. Minimal viable but clear.

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

GitHub Badge

Glama performs regular codebase and documentation scans to:

  • Confirm that the MCP server is working as expected.
  • Confirm that there are no obvious security issues.
  • Evaluate tool definition quality.

Our badge communicates server capabilities, safety, and installation instructions.

Card Badge

mcp-lighthouse-audit MCP server

Copy to your README.md:

Score Badge

mcp-lighthouse-audit MCP server

Copy to your README.md:

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/jladev/mcp-lighthouse-audit'

If you have feedback or need assistance with the MCP directory API, please join our Discord server