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sci-mcp

by qxkt222
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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 65 tools

    Disambiguation3/5

    Several tool pairs have overlapping purposes (e.g., plot vs plot_file, describe vs describe_file, forecast vs arima, analyze_dataset vs component tools). Descriptions clarify file-based vs inline and general vs specific, but the volume of tools makes misselection likely. Most tools remain distinct enough for an expert agent.

    Naming Consistency3/5

    All tool names use snake_case consistently, but the verb style is mixed: many are verb_noun (load_data, solve_heat_1d), while others are noun-only (arima, gradient, distribution). This is readable but lacks a single predictable pattern.

    Tool Count1/5

    With 65 tools, the server far exceeds the recommended 3-15 range and even the 25+ threshold for 'too many'. While the scientific computing domain is broad, the tool count is excessive and likely to overwhelm an agent, with many tools that could be consolidated.

    Completeness4/5

    The surface covers a vast range of scientific computing tasks: data IO, statistics, PDE solvers, optimization, signal processing, symbolic math, and more. Minor gaps exist (e.g., no dedicated data manipulation tools), but run_python provides a workaround.

    Maintenance

    ActivityMaintained
    ResponsivenessNo issues

    Resources