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MCP Python Interpreter

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    TDQS

    A3.5/5.0

    Scored across 10 tools

    Disambiguation4/5

    Most tools have distinct purposes, but there is some overlap between run_python_code and run_python_file that could cause confusion. The descriptions clarify that run_python_code handles code strings with multiple execution modes, while run_python_file executes existing files via subprocess, but both essentially execute Python code. Other tools like list_directory, read_file, and write_file are clearly distinct.

    Naming Consistency5/5

    All tool names follow a consistent verb_noun pattern using snake_case, such as clear_session, install_package, list_directory, etc. There are no deviations in naming conventions, making the set predictable and easy to understand.

    Tool Count5/5

    With 10 tools, the count is well-scoped for a Python interpreter server. It covers key operations like code execution, file management, package installation, and session handling without being overwhelming or insufficient for the domain.

    Completeness4/5

    The tool set provides comprehensive coverage for Python development tasks, including code execution, file operations, package management, and session control. Minor gaps exist, such as no explicit tool for deleting files or uninstalling packages, but agents can work around these using existing tools like write_file with overwrite or other methods.

    Maintenance

    ActivityInactive
    ResponsivenessSlow

    Resources