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yanislav-igonin

random-design-mcp

by yanislav-igonin
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TypeScript
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Server Quality Checklist

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

  • Latest release: v2.1.0

  • Disambiguation5/5

    The two tools have completely distinct purposes: one generates design descriptions, the other returns a version number. There is no overlap or ambiguity.

    Naming Consistency5/5

    Both tools follow a consistent verb_noun pattern using snake_case (generate_design_description, get_version), which is predictable and clear.

    Tool Count3/5

    With only two tools, the server feels very minimal for its stated purpose of random design generation. While it technically fulfills its core function, the lack of additional tools (e.g., for configuration or output variation) makes it feel incomplete.

    Completeness3/5

    The server covers its primary purpose (generating a design description) but lacks any supporting operations like listing past designs, customizing output, or handling multiple generations. The version tool is a minor utility, not core functionality.

  • Average 4.1/5 across 2 of 2 tools scored.

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

    • No community issues in the last 6 months
    • 11 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 passing

  • 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, the description carries full burden. It discloses that results are randomized and that 'compatibility' controls chaos, but does not mention side effects (e.g., mutation), permissions, rate limits, or return value details beyond 'Markdown design direction'.

    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 compact sentences. First sentence states core action, second provides critical usage condition. No superfluous words, front-loads the essential purpose.

    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?

    Given no output schema and no annotations, the description should clarify the return value (does it return the Markdown string or a reference?). It partially covers parameter usage but omits output structure and any behavioral constraints (e.g., rate limits, idempotency).

    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 description explains the semantics of 'compatibility' and states that 'audience', 'priority', and 'productType' should be inferred and passed in English. However, it does not specify allowed values or formats for these string parameters, and the schema has 0% coverage, leaving ambiguity.

    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 generates a randomized frontend design direction in English Markdown. It specifies the output format and distinguishes itself from the sibling tool 'get_version' which has a completely different purpose.

    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 gives specific guidance on when to set 'compatibility' to false (only on explicit user request for chaotic combinations) and explains that optional fields are inferred from context. While it does not explicitly list alternative tools, the sibling context makes the usage clear.

    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 provided, so description carries full burden. It discloses the returned value and context but omits potential error conditions (e.g., missing package.json) or any side effects. Adequate for a simple read-only operation.

    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?

    One sentence, 17 words, front-loaded with the action verb 'Return'. No unnecessary information.

    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?

    No output schema, but description explains what is returned (package version). For a simple tool, this is sufficient, though a note on version format would be helpful.

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

    Parameters4/5

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

    No parameters in input schema, so description does not need to add parameter-level details. Baseline of 4 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?

    Clearly states it returns the package version from package.json and specifies the purpose (debugging cache or publish state). Distinguishes well from the only sibling tool 'generate_design_description'.

    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?

    Implies usage scenarios (debugging) but does not explicitly state when not to use or provide alternatives. However, with only one sibling that serves a different purpose, confusion is minimal.

    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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