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priyankark

lighthouse-mcp

by priyankark
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Server Quality Checklist

58%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v1.0.0

  • Disambiguation5/5

    The two tools have clearly distinct purposes: get_performance_score retrieves a specific metric, while run_audit executes a full audit. There is no overlap or ambiguity between them.

    Naming Consistency5/5

    Both tools follow a consistent verb_noun pattern (get_performance_score, run_audit) with clear, descriptive names that align well with their functions.

    Tool Count2/5

    With only two tools, the server feels thin for a Lighthouse auditing domain. It lacks essential operations like getting other audit metrics (e.g., accessibility, SEO), running audits with custom configurations, or managing audit history.

    Completeness2/5

    The toolset is severely incomplete for Lighthouse functionality. It misses core features such as retrieving full audit reports, accessing other performance categories, configuring audits, or batch processing URLs, leaving significant gaps for agent workflows.

  • Average 2.9/5 across 2 of 2 tools scored.

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

    • 2 of 3 community issues answered or closed 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 is failing

  • 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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  ]
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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?

    With no annotations provided, the description carries the full burden of behavioral disclosure but offers minimal information. It doesn't describe whether this is a read-only operation, what authentication might be required, rate limits, error conditions, or what format the performance score returns. The description only states what the tool does, not how it behaves.

    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 extremely concise - a single sentence that directly states the tool's core functionality. There's zero waste or unnecessary elaboration, making it easy to parse and understand at a glance.

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

    Completeness2/5

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

    For a tool with no annotations and no output schema, the description is insufficiently complete. It doesn't explain what 'performance score' entails (numeric value, rating scale, composite metric), doesn't mention the sibling tool relationship, and provides no behavioral context. Users need more information to effectively use this tool.

    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?

    With 100% schema description coverage, the input schema already fully documents both parameters (url and device). The description adds no additional parameter semantics beyond what's in the schema - it doesn't explain what 'performance score' means, how it's calculated, or provide context about the device parameter's impact. Baseline 3 is appropriate when the schema does the heavy lifting.

    Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

    Purpose4/5

    Does the description clearly state what the tool does and how it differs from similar tools?

    The description clearly states the tool's purpose with a specific verb ('Get') and resource ('performance score for a URL'), making it immediately understandable. However, it doesn't distinguish this tool from its sibling 'run_audit' - both likely relate to performance analysis but with different outputs or scopes.

    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 about when to use this tool versus its sibling 'run_audit' or any alternatives. It doesn't mention prerequisites, constraints, or appropriate contexts for usage beyond the basic functionality stated.

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

  • Behavior2/5

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

    With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions running an audit but doesn't cover critical aspects like execution time, resource consumption, rate limits, authentication needs, or what the output entails (e.g., scores, reports). This leaves significant gaps for an agent to understand the tool's behavior.

    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, efficient sentence that directly states the tool's purpose without any fluff or redundant information. It's appropriately sized and front-loaded, making it easy to parse quickly.

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

    Completeness2/5

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

    Given the complexity of running an audit (which can be resource-intensive and produce detailed results), the lack of annotations, and no output schema, the description is insufficient. It doesn't explain what the audit returns (e.g., scores, reports, errors) or address behavioral traits like execution constraints, making it incomplete for effective agent use.

    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 has 100% description coverage, detailing all four parameters (url, categories, device, throttling) with defaults and options. The description adds no parameter-specific information beyond what's in the schema, so it meets the baseline score of 3 without compensating or adding extra value.

    Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

    Purpose4/5

    Does the description clearly state what the tool does and how it differs from similar tools?

    The description clearly states the action ('Run a Lighthouse audit') and target ('on a URL'), providing a specific verb+resource combination. However, it doesn't differentiate from the sibling tool 'get_performance_score', which might offer overlapping functionality for performance measurement, so it doesn't reach the highest score.

    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_performance_score', nor does it mention any prerequisites or exclusions. It simply states what the tool does without contextual usage information.

    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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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.

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