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Patrizzos

StyleSpeak

by Patrizzos
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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: v0.1.0

  • Disambiguation5/5

    Both tools have clearly distinct purposes: one resolves all CSS properties for a selector, the other traces a single property across files. No overlap.

    Naming Consistency5/5

    Both use consistent snake_case with verb_noun pattern (resolve_styles, trace_property), adhering to a clear naming convention.

    Tool Count3/5

    Two tools is on the low side, but appropriate for a focused CSS analysis utility. Could benefit from additional tools like listing files or selectors, but scope is narrow.

    Completeness4/5

    Covers the core use cases of understanding cascade and property conflicts. Minor gap: no tool to enumerate available selectors or files, but the stated purpose is well-served.

  • 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
    • 10 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.

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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?

    Discloses key behavioral trait of confidence levels due to no real DOM, which adds context beyond a simple read. No annotations exist, so description carries full burden; it sufficiently addresses limitations.

    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 efficient sentences with no superfluous information. First sentence states core purpose, second adds usage guidance and behavioral nuance. Front-loaded and to the point.

    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 tool complexity (CSS cascade) and no output schema, description outlines expected return values (properties, winner, overridden, confidence). Sufficient for an agent to gauge use, though could mention error handling or file discovery behavior.

    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%, so description adds minimal new semantics beyond schema. The description reinforces the overall behavior but does not enhance parameter understanding beyond what 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 resolves CSS for a given selector, listing specific outputs (properties, winning rule, overridden rules) and distinguishes from sibling 'trace_property' by focusing on full cascade context.

    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?

    Explicitly advises calling this before modifying styles to understand cascade context, providing clear usage guidance. Does not mention alternatives explicitly but implies it for initial diagnosis.

    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?

    Without annotations, the description fully discloses the tool's behavior: it traces properties across files, groups competing rules, and shows winners. It implies a read-only analysis. It could mention file type restrictions or performance notes, but the existing detail is sufficient for safe invocation.

    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: the first explains functionality succinctly, the second gives use cases. Every sentence adds value, no fluff. It is front-loaded with the core action.

    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 no output schema, the description adequately covers the tool's purpose and input requirements. It details what the tool does with the input (grouping, showing winners). It could briefly describe the output format, but it is mostly complete for a read-only analysis 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?

    Schema description coverage is 100%, so baseline is 3. The description adds no extra meaning beyond the schema; it only restates that files are provided and property is traced. It does not elaborate on the optional projectRoot parameter or provide examples.

    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 specific verbs ('traces', 'finding', 'grouping', 'showing') and clearly identifies the resource ('CSS property across all provided files'). It details the outcome (every rule, competing rules grouped, winner shown) and distinguishes from the sibling tool 'resolve_styles' by focusing on properties vs. general styling.

    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 explicitly states when to use the tool: 'to understand the blast radius of a property before changing it, or to find out why a property value isn't applying as expected.' It does not provide negative use cases or alternatives, but the context is clear and actionable.

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

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