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tatemz

habitica-mcp

by tatemz
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TypeScript
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Server Quality Checklist

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

  • Latest release: v0.0.0

  • Disambiguation5/5

    With only one tool, there are no other tools to confuse it with, so ambiguity is nonexistent.

    Naming Consistency5/5

    A single tool cannot be inconsistent; the name 'GreetingTool' follows a clear PascalCase convention.

    Tool Count1/5

    A single trivial tool is far too few for a server named 'habitica-mcp', which implies a broader functionality.

    Completeness1/5

    The tool is a mere greeting and completely unrelated to Habitica's capabilities, leaving no meaningful operations.

  • Average 2.4/5 across 1 of 1 tools scored.

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

    • No community issues in the last 6 months
    • 16 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.

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

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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?

    The description claims a non-destructive, deterministic action, but annotations indicate destructiveHint=true and openWorldHint=true, implying potential side effects. The description does not clarify this contradiction, leaving the agent uncertain about tool behavior.

    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 a single sentence with no filler, achieving high conciseness. However, it could be slightly expanded to improve clarity without being verbose.

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

    Completeness1/5

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

    With no output schema, no parameter descriptions, and annotations that contradict the description, the tool definition is incomplete. The agent lacks understanding of return values, behavioral details, and proper usage.

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

    Parameters1/5

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

    Schema coverage is 0%, and the description does not mention the 'name' parameter at all. While the parameter is self-explanatory, the description fails to add any semantic value beyond the schema.

    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 states 'Return a deterministic greeting for a Habitica MCP caller,' which clearly identifies the verb (return) and resource (greeting) with context. However, it could be more specific about the nature of the greeting.

    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?

    No usage guidance is provided. There is no mention of when to use this tool, prerequisites, or alternatives. With no siblings, differentiation is not needed, but the absence of context for invocation reduces clarity.

    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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