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TrainingPeaks MCP Server

TrainingPeaks MCP Server

by banananovej-chuan
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Remote

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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

    A4/5.0

    Scored across 14 tools

    Disambiguation5/5

    Each tool serves a distinct purpose without overlap. For example, tp_get_fitness returns daily CTL/ATL/TSB, tp_fitness_trend projects future values, and tp_training_load_summary provides weekly/monthly TSS totals. Similarly, tp_get_workout gives raw data while tp_workout_analysis computes derived metrics. Auth tools are separate, and each analytics tool focuses on a unique aspect.

    Naming Consistency4/5

    All tools follow a tp_ prefix with snake_case naming. The verb usage is mostly consistent, with many using 'get_' (e.g., tp_get_fitness, tp_get_workouts) and others using descriptive verbs like 'fitness_trend' or 'performance_summary'. Some names like tp_auth_status or tp_training_zones_distribution are nouns rather than verbs, but overall the pattern is predictable.

    Tool Count5/5

    14 tools cover a broad yet focused range of fitness data operations including authentication, profile, workouts (planned and completed), fitness metrics, trends, peaks/PRs, and detailed analysis. Each tool earns its place without redundancy or excessive specialization.

    Completeness5/5

    The tool set provides comprehensive coverage for a read-only analytics server. It includes auth, profile, workout retrieval (planned and historical), fitness metrics, trends, peaks/PRs, workout analysis, training load summary, and zones distribution. No obvious gaps for the domain.

    Maintenance

    ActivityInactive
    ResponsivenessNo issues

    Resources