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ccmdi

Obsidianki MCP Server

by ccmdi
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Python
Local

Server Quality Checklist

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

  • Latest release: v0.1.1

  • Disambiguation5/5

    Only one tool exists, so there is no possibility of confusion between tools. The tool's purpose is clearly defined.

    Naming Consistency5/5

    The single tool uses a clear verb_noun pattern ('generate_flashcards'), which is consistent within the server.

    Tool Count3/5

    The server has only one tool, which is the bare minimum. For a single-purpose server focused on generating flashcards, this is borderline but not excessive.

    Completeness2/5

    The tool only supports generation. There are no tools for viewing, updating, or deleting flashcards, creating significant gaps for any workflow beyond initial generation.

  • Average 3.6/5 across 1 of 1 tools scored.

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

    • No community issues in the last 6 months
    • 0 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.

  • 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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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?

    No annotations are provided, so the description carries full burden. It does not disclose behavioral traits such as permissions required, side effects (e.g., file creation/modification), safety guarantees, or expected behavior when parameters are omitted.

    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 concise with a single line purpose followed by a clear, well-structured parameter list. It is front-loaded and each sentence contributes, though could be slightly tighter.

    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 the presence of an output schema (context signal), the description is mostly complete. However, it lacks explanation of what happens when both notes and query are empty, and could clarify the output format more, though the output schema may cover that.

    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?

    With 0% schema description coverage, the description adds significant value by explaining each parameter: notes supports glob with sampling, cards has a recommended range, query is for new content, deck defaults, and use_schema reuses existing cards, all beyond the schema's type/default information.

    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 starts with 'Generate flashcards using obsidianki,' which clearly states the verb (generate) and resource (flashcards). It is specific and easily understood, with no ambiguity.

    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 provides usage hints for individual parameters (e.g., 'You can leave this blank if the user does not specify' for notes, 'recommend 3-6 if set' for cards), but lacks explicit guidance on when to use this tool versus alternatives or overall context for its application.

    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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