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Fast Embedding MCP SSE

Fast Embedding MCP SSE

by Rikka-Botan
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Python
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    TDQS

    A3.8/5.0

    Scored across 7 tools

    Disambiguation5/5

    Each tool serves a distinct function: embedding, similarity computation, stateless search, and index management. The index_* tools are clearly separated from stateless operations, and search vs index_query are differentiated by whether a persistent index is used.

    Naming Consistency3/5

    Naming mixes styles: embed_text uses verb_noun, index_add uses noun_verb, and similarity/index_stats are nouns. While the index_ prefix provides consistency for index operations, the overall pattern is inconsistent.

    Tool Count5/5

    7 tools is well-scoped for an embedding server, covering core operations without redundancy. Each tool earns its place, and the count is within the ideal range.

    Completeness4/5

    Core workflows are covered: text embedding, similarity scoring, stateless search, and a persistent index with add/query/clear. Missing per-document delete/update or retrieval by ID are minor gaps that don't break typical usage.

    Maintenance

    ActivityInactive
    ResponsivenessNo recent community issues

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