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spacecat398

TinyRAG

by spacecat398
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Python
Local

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    TDQS

    A3.9/5.0

    Scored across 3 tools

    Disambiguation3/5

    query_knowledge_base and search_relevant_chunks both search the knowledge base for relevant content, differing only in whether the response is generated or raw chunks. An agent might select the wrong one if not attentive. list_documents is clearly distinct.

    Naming Consistency5/5

    All tool names follow a consistent verb_noun pattern using snake_case: query_, list_, search_. The naming is predictable and clear.

    Tool Count5/5

    Three tools is an appropriate size for a focused RAG server, covering retrieval and listing without unnecessary bulk.

    Completeness3/5

    The tools cover querying and listing but lack document management (add, update, delete). This is a notable gap for a knowledge base server, though the core retrieval workflow is present.

    Maintenance

    ActivityInactive
    ResponsivenessNo issues

    Resources