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Server Quality Checklist

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  • Latest release: v0.1.0

  • Disambiguation5/5

    The two tools have clearly distinct purposes: one implements UI changes via an iterative loop, the other reviews the UI read-only without mutations. No overlap in functionality.

    Naming Consistency5/5

    Both tools follow a consistent 'ui_verb' pattern (ui_implement, ui_review) with snake_case, making them predictable and easy to understand.

    Tool Count5/5

    Two tools is appropriate for a focused server that handles UI implementation and review. Each tool serves a distinct, well-scoped purpose without unnecessary extras.

    Completeness4/5

    The tool set covers the core workflow of implementing and reviewing UIs. A minor gap might be a tool for managing scope or configuration, but the current set is sufficient for the stated purpose.

  • Average 4.6/5 across 2 of 2 tools scored.

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

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

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    {
      "$schema": "https://glama.ai/mcp/schemas/server.json",
      "maintainers": [
        "your-github-username"
      ]
    }

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Servers are automatically synced at least once per day, but you can also sync manually at any time to instantly update the server profile.

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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

  • Behavior5/5

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

    The description fully discloses the iterative workflow, including creating a git worktree, starting a dev server, taking screenshots, diff-gating, and cleanup. It explains the effect of the 'apply' parameter on whether files are written back. No annotations are provided, so the description carries the full burden and meets it well.

    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 detailed but well-structured with a summary, numbered workflow, args list, and returns section. It is front-loaded with the core purpose. While long, the complexity of the tool justifies the length. Could be slightly more concise, but very effective.

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

    Completeness5/5

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

    Given the tool's complexity, 7 parameters, no annotations, and the presence of an output schema, the description covers all necessary aspects: purpose, workflow, parameter meanings, and return value structure. No gaps are evident.

    Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

    Parameters5/5

    Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

    With 0% schema description coverage, the description compensates with a dedicated 'Args:' section that explains each parameter's meaning, defaults, and behavior. For example, 'project_dir' is described as 'Absolute path to the project root (must contain the scope)' and 'match_threshold' is explained in context of design_refs.

    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 begins with a clear verb 'Implement/modify UI' and specifies the resource 'UI for task' and method 'iterative, diff-gated vision loop'. It distinguishes from sibling tool 'ui_review' by detailing an implementation workflow rather than review.

    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 implementing UI changes via a vision loop, but does not explicitly contrast with sibling tool 'ui_review' or state when each should be used. No exclusion criteria or alternatives are mentioned.

    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?

    With no annotations, the description carries full burden. It explains the write behavior, including what happens when overwrite is false (leaves existing file untouched) and true (clobbers). It describes reading project files and the return format. However, it could mention that it reads files non-destructively.

    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 well-structured with a concise summary, a paragraph explaining the purpose, then explicit Args and Returns sections. Every sentence adds value and there is no 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?

    Given the tool has an output schema but the description already lists return fields, it is fairly complete. It covers purpose, parameters, and return. It could mention potential prerequisites (e.g., existence of package.json), but not necessary for a setup tool.

    Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

    Parameters5/5

    Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

    Schema coverage is 0%, so description compensates fully. For project_dir, it says 'Path to the project root to scan and write into.' For overwrite, it explains the behavior in detail, including the status returned when not overwriting. This adds significant meaning beyond the schema.

    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 auto-detects the stack and writes a config file, with specific actions like inspecting manifests and guessing framework. It distinguishes itself from sibling tools (ui_implement, ui_review) by noting that after this tool, the others run with no further setup.

    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 says it's a 'zero-to-config helper for non-technical users' and implies usage before ui_implement/ui_review. It doesn't explicitly state when not to use it, but the context makes it clear that it's for initial setup. An explicit 'when not to use' would improve it.

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

  • Behavior5/5

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

    With no annotations, the description fully discloses behavioral traits: it never mutates (edits reverted via diff-gate), a11y runs on web only, and it returns a detailed dict. No contradictions.

    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 well-structured with paragraphs and a clear Args section, but a few sentences could be trimmed without losing clarity. Still concise overall.

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

    Completeness5/5

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

    Given the tool's complexity and presence of an output schema, the description covers inputs, behavior, edge cases (native vs web), and return structure thoroughly. No gaps.

    Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

    Parameters5/5

    Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

    Schema coverage is 0%, but the description explains all four parameters clearly, including defaults (a11y) and types (against_design as paths). This adds significant meaning beyond the schema.

    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 ('serve, screenshot, critique') and clearly identifies the resource (app UI). It distinguishes from the sibling tool 'ui_implement' by emphasizing the read-only review nature.

    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 implies use for design review without mutation, and explains a11y behavior per platform. It does not explicitly state when not to use or list alternatives, but the sibling tool 'ui_implement' provides context.

    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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