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mcp-ollama-qdrant

by sangingeum
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Python
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    Deploy Server
    One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

    TDQS

    A3.9/5.0

    Scored across 6 tools

    Disambiguation5/5

    Each tool targets a distinct operation: save (single), save_memories (batch), search, update, delete, and list_collections. The single-vs-batch split between save_memory and save_memories is explicitly distinguished in the descriptions, so an agent can reliably choose.

    Naming Consistency5/5

    All tools follow a consistent snake_case verb_noun pattern (save_memory, search_memory, update_memory, delete_memory, save_memories, list_collections). The only variation, list_collections, reflects a genuinely different resource (collections vs memories), not an inconsistent style.

    Tool Count5/5

    Six tools is well-scoped for a vector-DB memory server, covering the full point lifecycle plus collection listing without redundancy. Nothing feels padded or missing at the count level.

    Completeness4/5

    Core memory lifecycle (create, batch create, search, update, delete) and collection listing are all present, making the surface largely complete. Minor gaps remain: no get_memory-by-ID retrieval and no collection deletion/creation management beyond implicit auto-creation.

    Maintenance

    ActivityMaintained
    ResponsivenessNo issues

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