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mcp-numpy

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    TDQS

    B3.1/5.0

    Scored across 72 tools

    Disambiguation5/5

    Each tool targets a distinct NumPy function with clear, specific descriptions. No two tools perform the same operation; even closely related functions like np_percentile and np_quantile are differentiated by definition and use case.

    Naming Consistency4/5

    Most tools follow a consistent 'np_operation' pattern. The only exception is 'npastype' which lacks an underscore, and 'np_linalg_norm' uses a submodule prefix while other linear algebra tools (np_eig, np_inv, etc.) do not. Overall, the pattern is clear and predictable.

    Tool Count2/5

    72 tools is excessive for an MCP server, making selection difficult for an agent. While the server aims to cover a broad library like NumPy, the count far exceeds the typical well-scoped range (3-15) and even the 'too many' threshold (25+).

    Completeness3/5

    The tools cover core array operations, math, statistics, linear algebra, and random sampling. However, common functions like sorting (np_sort), unique (np_unique), where (np_where), and fft are missing, representing notable gaps for a numpy-oriented server.

    Maintenance

    ActivityInactive
    ResponsivenessNo issues

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