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teslamate-mcp

by cobanov
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Python
Hybrid

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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

    A3.6/5.0

    Scored across 18 tools

    Disambiguation4/5

    Most tools have distinct purposes focusing on different aspects of Tesla vehicle data (charging, driving, battery, efficiency, locations, etc.), though some like 'get_battery_degradation_over_time' and 'get_battery_health_summary' could potentially overlap in scope, and 'get_daily_driving_patterns' and 'get_drive_summary_per_day' might be confused for similar daily analyses. Descriptions help clarify, but there is minor ambiguity in a few cases.

    Naming Consistency5/5

    All tool names follow a consistent 'get_' prefix with descriptive snake_case nouns, such as 'get_all_charging_sessions_summary' and 'get_current_car_status'. This uniform pattern makes the tool set predictable and easy to navigate, with no deviations in naming conventions.

    Tool Count4/5

    With 18 tools, the count is slightly high but reasonable for a comprehensive Tesla data analytics server, covering various metrics like charging, driving, battery, and efficiency. It might feel a bit heavy, but each tool appears to serve a specific purpose within the domain, avoiding redundancy.

    Completeness5/5

    The tool set provides extensive coverage for analyzing Tesla vehicle data, including real-time status, historical trends, charging patterns, driving habits, battery health, efficiency metrics, and anomaly detection. There are no obvious gaps; it supports a full lifecycle of data retrieval and analysis without dead ends for the stated purpose.

    Maintenance

    ActivitySlowing
    ResponsivenessSlow

    Resources