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Opti-MCP

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    Deploy Server
    Shareable, User-Owned Context BackendPeople, applications, and agents read, write, and coordinate together in one context lake. Reach it over MCP, a CLI, a Python SDK, or REST.fulcradynamics.comAd

    TDQS

    B3.2/5.0

    Scored across 3 tools

    Disambiguation4/5

    The three tools target distinct problem classes (LP, IP/MIP, knapsack), and descriptions make the distinct semantics clear. However, knapsack is technically a special case of integer programming, and LP is a relaxation of MIP, so boundaries overlap enough that a user might wonder which solver to pick.

    Naming Consistency5/5

    All tools follow a clean solve_<problem> verb_noun pattern: solve_linear_program, solve_integer_program, solve_knapsack. The convention is predictable and readable throughout.

    Tool Count3/5

    Three tools is thin for a general optimization server; it covers only LP, IP/MIP, and a single specialized problem. The count is borderline rather than well-scoped for the apparent breadth of the domain.

    Completeness3/5

    Core LP and MIP solving are covered, plus one special case, giving basic coverage with no dead ends for those tasks. However, common optimization operations such as constraint programming, quadratic/nonlinear programming, assignment/transportation, and scheduling are absent, leaving notable gaps.

    Maintenance

    ActivityInactive
    ResponsivenessNo issues

    Resources