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zvec-ai

zvec-mcp-server

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by zvec-ai

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    TDQS

    A4/5.0

    Scored across 17 tools

    Disambiguation5/5

    Each tool targets a distinct operation: collection lifecycle (create/open/destroy/info/optimize), document CRUD (insert/update/upsert/delete/fetch), index management (create/drop), and search variants (vector_query, embedding_search, multi_vector_query). There is no apparent overlap or ambiguity.

    Naming Consistency5/5

    All tools follow a consistent snake_case verb_noun pattern (e.g., create_and_open_collection, fetch_documents, generate_dense_embedding). There is no mixing of conventions, and the naming clearly conveys the action and resource.

    Tool Count5/5

    17 tools cover the full vector database workflow without being excessive. Each tool serves a clear purpose, from collection management to high-level embedding search, making the surface appropriately scoped.

    Completeness4/5

    The tool set covers most essential operations: collection CRUD, document CRUD, indexing, multiple search modes, and embedding generation. A minor gap is the lack of a tool to list all available collections, which may require external file system access, but the core workflow is complete.

    Maintenance

    ActivityInactive
    ResponsivenessUnresponsive