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Server Quality Checklist

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  • Latest release: v1.0.0

  • Disambiguation4/5

    Most tools have distinct purposes targeting specific Strava resources (activities, segments, routes, clubs, gear) with clear action verbs. However, some overlap exists: 'strava_get_activity_streams' and 'strava_get_segment_effort_streams' both provide time-series data, which could cause confusion about when to use each. The 'strava_list_activity_zones' and 'strava_get_athlete_zones' also cover similar zone concepts but for different contexts.

    Naming Consistency5/5

    Tool names follow a highly consistent 'strava_verb_noun' pattern throughout, using snake_case uniformly. The verbs are predictable (get, list, create, update, star, export) and align well with the resource types (activity, segment, route, club, athlete, gear). This makes the tool set easy to navigate and understand at a glance.

    Tool Count3/5

    With 33 tools, the count feels heavy for a single-domain server, potentially overwhelming for agents. While Strava's API is feature-rich, this many tools may indicate over-fragmentation (e.g., separate tools for similar stream types or list functions). A more consolidated set (e.g., 15-20 tools) could improve usability without losing functionality.

    Completeness5/5

    The tool set provides comprehensive coverage of Strava's core domain, including full CRUD/lifecycle operations for activities (create, get, list, update), segments (explore, get, star, efforts), routes (get, export), clubs (get, list members/admins/activities), and athlete data (profile, stats, zones). It handles key workflows like file uploads, streams, and social features (comments, kudos), leaving no obvious gaps.

  • Average 3.7/5 across 33 of 33 tools scored.

    See the Tool Scores section below for per-tool breakdowns.

    • No community issues in the last 6 months
    • 0 commits in the last 12 weeks
    • No stable releases found
    • No critical vulnerability alerts
    • No high-severity vulnerability alerts
    • No code scanning findings
    • CI status not available
  • This repository is licensed under ISC License.

  • This repository includes a README.md file.

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How is the quality score calculated?

The overall quality score combines two components: Tool Definition Quality (70%) and Server Coherence (30%).

Tool Definition Quality measures how well each tool describes itself to AI agents. Every tool is scored 1–5 across six dimensions: Purpose Clarity (25%), Usage Guidelines (20%), Behavioral Transparency (20%), Parameter Semantics (15%), Conciseness & Structure (10%), and Contextual Completeness (10%). The server-level definition quality score is calculated as 60% mean TDQS + 40% minimum TDQS, so a single poorly described tool pulls the score down.

Server Coherence evaluates how well the tools work together as a set, scoring four dimensions equally: Disambiguation (can agents tell tools apart?), Naming Consistency, Tool Count Appropriateness, and Completeness (are there gaps in the tool surface?).

Tiers are derived from the overall score: A (≥3.5), B (≥3.0), C (≥2.0), D (≥1.0), F (<1.0). B and above is considered passing.

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