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fgbytes

sansfiction-mcp

by fgbytes
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JavaScript
Remote

Server Quality Checklist

92%
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  • Latest release: v1.0.2

  • Disambiguation5/5

    All tools have distinct purposes: they either handle public catalog data (get_book, search_books, get_series, list_public_collections) or personal library operations (list_my_books, add_book_to_library, etc.). There is no ambiguity or functional overlap between any pair of tools.

    Naming Consistency4/5

    Names follow a consistent verb_noun pattern (get_book, search_books, add_book_to_library, etc.) with occasional longer forms. The only minor inconsistency is using 'my' in some names (list_my_books) without it in others (add_book_to_library vs list_public_collections), but the pattern is clear overall.

    Tool Count5/5

    11 tools is well-scoped for a reading/bookshelf management server. Each tool addresses a distinct operation (search, CRUD for library, statistics, collections) without excess or deficiency.

    Completeness5/5

    The tool surface covers all core workflows: discovering books (search, get, series, collections), managing a personal library (add, update, remove, search, list), and tracking reading progress (stats). There are no obvious gaps for the stated purpose of a personal book catalog.

  • Average 2.8/5 across 11 of 11 tools scored.

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

    • No community issues in the last 6 months
    • 1 commit 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.

  • Tools from this server were used 6 times in the last 30 days.

  • This repository includes a glama.json configuration file.

  • This server has been verified by its author.

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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

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  • Confirm that the MCP server is working as expected.
  • Confirm that there are no obvious security issues.
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