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DOGGY-SAINT

EndNote Library Reader

by DOGGY-SAINT
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Server Quality Checklist

58%
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  • Latest release: v0.1.0

  • Disambiguation3/5

    There is some overlap between list_papers and search_papers, as both return lists of papers with similar metadata, which could cause confusion about when to use each. However, read_paper and refresh_backup have distinct purposes, and the descriptions help clarify that search_papers is for fuzzy title/keyword searches while list_papers is for paginated listing.

    Naming Consistency5/5

    All tool names follow a consistent verb_noun pattern with snake_case, such as list_papers, read_paper, refresh_backup, and search_papers. This makes the tool set predictable and easy to understand.

    Tool Count4/5

    With 4 tools, the count is reasonable for a library reader focused on paper retrieval and management. It covers core operations like listing, searching, reading, and backup refresh, though it might be slightly thin if more advanced features like updating or deleting papers were expected.

    Completeness3/5

    The tool set covers basic read operations (list, search, read) and a utility (refresh_backup), but lacks CRUD completeness for a paper management system, as there are no tools for creating, updating, or deleting papers. This could lead to gaps in agent workflows that require full lifecycle management.

  • Average 4.4/5 across 4 of 4 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.

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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.

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