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superpos-mcp

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by Superpos-AI
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Python
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    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 42 tools

    Disambiguation5/5

    Each tool has a clearly distinct purpose, with specific names like superpos_create_task vs superpos_create_issue, and descriptions that leave no ambiguity. Even related tools like poll_tasks and poll_events are easily differentiated by their target.

    Naming Consistency5/5

    All 42 tools follow the consistent pattern superpos_verb_noun in snake_case, with verbs like get, create, update, delete, list, poll, etc. This makes the API predictable and easy to navigate.

    Tool Count4/5

    At 42 tools, the count is high but appropriate for the broad scope of the server, which covers agent identity, task management, knowledge, schedules, issues, tracks, and hive exploration. A few tools could potentially be merged, but overall the granularity is justified.

    Completeness5/5

    The tool surface covers the full lifecycle of tasks, issues, tracks, schedules, and knowledge, plus extras like agent status, events, and hive mapping. No obvious gaps are apparent for the stated purpose of a multi-agent collaboration platform.

    Maintenance

    ActivityInactive
    ResponsivenessNo issues

    Resources