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profilekit-mcp

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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: v0.2.1

  • Disambiguation5/5

    Each tool has a distinct purpose: list_cards discovers available card types, list_themes retrieves theme options, and render generates card URLs. There is no overlap between them.

    Naming Consistency4/5

    Tools follow a consistent 'verb_noun' pattern with 'list_' for listing and 'render' for generation. 'render' is a bare verb while others have prefix, but it's minor.

    Tool Count5/5

    With 3 tools covering listing, theming, and rendering, the scope is tight and focused. Each tool is essential and the count is appropriate for a card generation server.

    Completeness3/5

    The tools cover the core workflow of discovering cards and themes and generating URLs, but there is no tool for updating or deleting cards (if such operations exist), nor for fetching the rendered SVG directly.

  • Average 4.4/5 across 3 of 3 tools scored.

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

    • 0 of 1 community issues answered or closed in the last 6 months
    • 21 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.

  • 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

  • Behavior3/5

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

    With no annotations, description carries full burden. Discloses it lists only built-in themes, and hints at card integration. No mention of whether it returns names only or full details, but adequate for a simple list tool.

    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 sentences, front-loaded with purpose and examples, no fluff. Every word earns its place.

    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?

    For a parameterless list tool with no output schema, description is sufficient. Explains purpose, examples, and integration with cards. Could mention if output is sorted or filtered, but not critical.

    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?

    Schema has no parameters (coverage 100%), and description adds value by explaining how the output relates to card usage, providing context beyond the empty 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?

    Clearly states it lists built-in ProfileKit themes and provides specific examples (dark, tokyo_night, kanagawa, rose_pine). Distinguishes from sibling tools like list_cards and render.

    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?

    Explains how to use themes with cards via ?theme= parameter, and mentions alternative ?theme_url= for custom palettes. Could be more explicit about when to choose each option.

    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?

    No annotations are provided, so the description carries the full burden. It clearly states the tool does NOT fetch the SVG, which is a key behavioral trait. However, it does not mention side effects, permissions, or rate limits, which would be needed for full transparency. The explicit negation of SVG fetching earns a high score, but some gaps remain.

    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 long, highly concise, and front-loads the core purpose. Every sentence provides essential information without redundancy.

    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 no output schema and moderate complexity (nested params), the description adequately explains the tool's output (URL and snippets) and workflow (call list_cards first). It could mention the output format in more detail, but the context signals (no output schema) mean the description carries this burden well.

    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 minimal extra meaning beyond the schema: it explains that values are stringified and URL-encoded, and suggests using list_cards output for common params. This adds some value but not enough to raise the score above baseline.

    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 builds a ProfileKit card URL and markdown/HTML snippets, specifying the verb 'Build' and the resource 'ProfileKit card URL plus snippets'. It distinguishes from 'list_cards' by noting it does NOT fetch the SVG, which helps clarify the tool's scope.

    Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

    Usage Guidelines5/5

    Does the description explain when to use this tool, when not to, or what alternatives exist?

    The description explicitly advises to call 'list_cards' first if unsure about the card type or parameters, providing clear when-to-use and when-not-to-use guidance. It also mentions that the URL is for embedding, not fetching SVG, setting correct expectations.

    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?

    No annotations exist, so the description must convey behavior. It states the catalog is fetched live from ProfileKit on first call and cached per process, which is useful context. A slight deduction for not mentioning if listing is read-only (though implied).

    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?

    Three sentences, each earning its place: first explains what it does, second when to use it, third a behavioral note. No wasted words.

    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 zero parameters and no output schema, the description covers purpose, usage, and behavior completely. No gaps.

    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?

    The input schema has no parameters, and schema description coverage is 100% (vacuously). The description adds meaning by describing what the output contains (one-line description, required params) beyond the empty 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 states the tool lists every ProfileKit card type with a one-line description and required params. It specifies the exact resource (card types) and action (list), clearly distinguishing it from siblings like 'render'.

    Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

    Usage Guidelines5/5

    Does the description explain when to use this tool, when not to, or what alternatives exist?

    The description explicitly says to use this tool before calling 'render' when the user asks what cards exist or which to use. This provides clear context for when to use it versus alternatives.

    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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