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roizenlabs

SportIntel MCP Server

by roizenlabs
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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 4 tools

    Disambiguation5/5

    Each tool has a clearly distinct purpose with no overlap: explain_recommendation focuses on model explainability, get_live_odds on betting odds, get_player_projections on player forecasts, and optimize_lineup on lineup construction. The descriptions reinforce these unique roles, making tool selection straightforward for an agent.

    Naming Consistency5/5

    All tool names follow a consistent verb_noun pattern (explain_recommendation, get_live_odds, get_player_projections, optimize_lineup) with clear, descriptive verbs. There are no deviations in style or convention, ensuring predictable and readable naming throughout the set.

    Tool Count4/5

    With 4 tools, the count is reasonable for a sports analytics server, covering key areas like projections, odds, explainability, and optimization. It's slightly lean but well-scoped; adding tools for historical data or team-level analysis could enhance completeness without being necessary.

    Completeness4/5

    The tools provide solid coverage for DFS and betting workflows, including projection generation, odds retrieval, lineup optimization, and model explainability. Minor gaps exist, such as missing historical performance data or team-level projections, but agents can work effectively with the current surface for core tasks.

    Maintenance

    ActivityInactive
    ResponsivenessNo issues

    Resources