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MCP Self-Learning Server

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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

    C2.9/5.0

    Scored across 8 tools

    Disambiguation4/5

    Most tools have distinct purposes, such as analyze_pattern for pattern analysis, export_knowledge for exporting, and get_insights for insights. However, get_insights and get_performance_metrics could be slightly overlapping, as insights might include performance data, but their descriptions help differentiate them.

    Naming Consistency5/5

    All tool names follow a consistent verb_noun pattern with clear, descriptive verbs like analyze, export, get, import, optimize, predict, and trigger. The naming is uniform and predictable throughout the set.

    Tool Count5/5

    With 8 tools, the count is well-scoped for a self-learning server, covering key operations like analysis, knowledge management, insights, and optimization. Each tool appears to serve a specific function without redundancy.

    Completeness4/5

    The tool set covers core self-learning functions, including analysis, knowledge import/export, insights, metrics, optimization, prediction, and manual triggering. A minor gap might be the lack of a tool for resetting or clearing knowledge, but overall, it supports the server's purpose effectively.

    Resources