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dmitryanchikov

mcp-optimizer

by dmitryanchikov
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Python
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    TDQS

    B3.4/5.0

    Scored across 13 tools

    Disambiguation3/5

    The three general solvers (linear, integer, mixed integer) overlap significantly since linear programming can also handle integer and binary variables, and mixed integer is essentially a superset of integer. The specialized problem solvers (assignment, transportation, knapsack, etc.) are distinct but could be modeled via the general tools, creating potential confusion. Descriptions help but boundaries are fuzzy.

    Naming Consistency3/5

    Most tools follow a 'solve_*_problem_tool' pattern, but several lack the '_tool' suffix (e.g., solve_mixed_integer_program, solve_traveling_salesman_problem) and one is 'validate_optimization_input' without 'solve' prefix. This inconsistency, while readable, violates a predictable verb_noun pattern.

    Tool Count5/5

    13 tools is well within the ideal 3-15 range. Each tool serves a specific optimization domain, and the count feels appropriate for a server dedicated to mathematical optimization without being overwhelming or sparse.

    Completeness4/5

    The server covers a wide range of classic optimization problems: LP, IP, MIP, assignment, transportation, knapsack, TSP, VRP, job shop, shift scheduling, portfolio, and production planning. Minor gaps exist, such as a generic constraint satisfaction tool or nonlinear optimization, but for the stated purpose the coverage is quite comprehensive.

    Maintenance

    ActivityInactive
    ResponsivenessNo issues

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