Skip to main content
Glama
OuchiniKaeru

Genetic Optimization MCP Server

by OuchiniKaeru
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

Related Servers

Alternatives to Genetic Optimization MCP Server

No user-submitted related servers found.

    Related Servers

    • CHUK MCP Solver

      IBM
      A
      license
      A
      quality
      F
      maintenance
      Provides constraint satisfaction and optimization capabilities to LLMs and AI agents for scheduling, resource allocation, routing, budget optimization, and configuration problems using Google OR-Tools CP-SAT solver.
      5
      5
      Apache 2.0

    • Optimizer

      meddhajji
      A
      license
      Not graded
      quality
      B
      maintenance
      Enables MCP-capable agents to solve scheduling and integer optimization problems using Google OR-Tools, return proven-optimal or valid solutions, independently verify proposed solutions, and explain infeasible rule sets.
      MIT

    • Vepathos MCP

      vizzito
      A
      license
      Not graded
      quality
      B
      maintenance
      Enables AI agents to submit asynchronous last-mile fleet optimizations that assign stops to vehicles and sequence routes from a depot, then retrieve status, summaries, ordered stop sequences, or unassigned ids. Supports hundreds to thousands of stops with optional weight, volume and time-window constraints, with results retained for 24 hours.
      Apache 2.0

    • RelayMe

      PrayYoung
      A
      license
      Not graded
      quality
      B
      maintenance
      Enables AI agents to safely run bounded, sandboxed tasks on remote machines with persistent state and reviewable artifacts.
      Apache 2.0
    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

    A3.7/5.0

    Scored across 7 tools

    Disambiguation5/5

    Each tool maps to a distinct stage of the optimization lifecycle: validate, submit, poll, list history, cancel, compare, and enumerate evaluators. The only potentially adjacent pair (solve vs. validate_problem) is clearly separated by the descriptions, since validation explicitly evaluates nothing. No two tools could plausibly be mistaken for one another.

    Naming Consistency5/5

    All tools share a uniform 'optimization.' namespace and use snake_case verb_noun naming (get_result, validate_problem, list_evaluators, list_runs, compare_results). The bare 'solve' is the sole noun-less verb but reads naturally as the primary action and does not break the pattern.

    Tool Count5/5

    Seven tools is well within the sweet spot and every tool earns its place: submission, polling, validation, enumeration, history, cancellation, and comparison are all genuinely needed. There is no filler or redundant surface.

    Completeness4/5

    The run lifecycle is fully covered end-to-end, including idempotency, async polling, cancellation, and cross-run comparison, which is unusually thorough. Minor gaps remain: no way to delete/prune run history or manage stored problems, and no streaming/partial-progress retrieval beyond status polling.

    Maintenance

    ActivityMaintained
    ResponsivenessNo issues

    Resources