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AI Workspace Kit

AI Workspace Kit

by alice840126-ship-it
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Python
Hybrid

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    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

    A3.8/5.0

    Scored across 5 tools

    Disambiguation5/5

    Each tool has a clearly distinct purpose: memory recall, checkpoint persistence, local document search, artifact reading, and health checking. The descriptions explicitly separate session memory (workspace_recall) from local document search (search_local) and clarify that read_local_artifact must follow search_local. This leaves little chance of misselection.

    Naming Consistency3/5

    All names use snake_case, but the set mixes two prefix families (workspace_ and local/search/read) and inconsistent verb/noun structures such as workspace_recall, workspace_checkpoint, workspace_health, search_local, and read_local_artifact. The names remain readable, but the pattern is not fully predictable.

    Tool Count5/5

    Five tools is well-scoped for a workspace memory and local-document kit. Each tool covers a distinct operation and none feels redundant or gratuitous. This is squarely within the ideal 3–15 range.

    Completeness4/5

    The surface covers core retrieval, persistence, local search/read, and health checking, which are the main workflows for this domain. Minor gaps remain around memory lifecycle operations such as updating, deleting, or listing checkpoints, but agents can likely work around these limitations.

    Maintenance

    ActivitySlowing
    ResponsivenessOutdated data

    Resources