strava-mcp-agent

Your AI running coach that actually remembers. An MCP server with persistent memory that connects your Strava data to Claude — tracking your zones, pace trends, injuries, and goals across sessions so coaching advice stays current.

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What it does

strava-mcp-agent gives Claude 18 tools — 13 for live Strava data and 5 for persistent memory that carries context across every conversation.

Memory & Context Tools

Tool	Description
get_athlete_context	Loads your full training profile at conversation start — HR zones, current Z2 pace, weekly mileage trends, goals, injuries. Auto-refreshes if stale.
update_athlete_profile	Save your max HR, resting HR, weight, FTP, and custom HR zones
update_athlete_goals	Track race targets, training phase, and deadlines
update_athlete_injuries	Log injuries, update recovery status, mark as resolved
add_training_note	Persistent coaching notes across sessions (last 50 kept)

Strava Data Tools

Tool	Description
get_athlete	Your profile — name, weight, FTP, bio
get_athlete_stats	All-time & recent totals for run/bike/swim
list_activities	Browse activities with date/type filtering
get_activity	Full detail for one activity (splits, HR, cadence, weather)
get_activity_zones	Heart-rate and power zone distribution
get_activity_laps	Per-lap pace, HR, and distance breakdown
get_activity_streams	Raw sensor data (GPS, HR, cadence, watts, altitude)
get_starred_segments	Your starred segments
get_segment_efforts	Efforts on a specific segment
get_routes	Routes you've created
get_gear	Shoe/bike details and mileage
get_clubs	Clubs you belong to
get_running_summary	AI-ready coaching summary (weekly mileage, pace trends, best efforts, HR stats)

Why memory matters

Without memory, Claude forgets everything between conversations:

• Session 1: "Your Z2 pace is 9:30/km"
• Session 2: "Your Z2 pace improved to 8:10/km"
• Session 3: "I recommend running at 9:00–9:30/km" ← wrong, forgot the improvement

With memory, fitness metrics are auto-computed from your Strava data and persist. Claude always knows your current zones, trends, and injuries.

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Quick Start (2 commands)

pip install strava-mcp-agent
strava-mcp-token

Step 1: Create a Strava API app (one time)

Go to strava.com/settings/api and fill in:

Field	What to enter
Application Name	Anything (e.g. My Claude MCP)
Category	Pick any
Club	Leave blank
Website	http://localhost
Authorization Callback Domain	localhost

The callback domain must be localhost — this is what allows the setup wizard to receive the authorization code on your machine.

Click Create. On the next page, copy your Client ID (a number like 123456) and Client Secret (a long code like abc123def456...).

Step 2: Run the setup wizard

strava-mcp-token

It will:

1. Ask for your Client ID and Client Secret
2. Open your browser — click Authorize on the Strava page
3. Auto-detect your OS (macOS / Linux / Windows)
4. Find the Python that has the package installed
5. Write the Claude Desktop config file for you

Step 3: Restart Claude Desktop

Your 18 tools are ready. To get the most out of coaching, start by telling Claude your max HR and goals:

"Set my max HR to 190, resting HR 48, and my goal is sub-50 10K by June"

Claude will save this and use it to compute accurate training zones from then on.

Manual setup (if you prefer)

<details> <summary>Click to expand</summary>

Add to your Claude Desktop config:

• macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
• Windows: %APPDATA%\Claude\claude_desktop_config.json
• Linux: ~/.config/Claude/claude_desktop_config.json

{
  "mcpServers": {
    "strava": {
      "command": "strava-mcp",
      "env": {
        "STRAVA_CLIENT_ID": "your_client_id",
        "STRAVA_CLIENT_SECRET": "your_client_secret",
        "STRAVA_REFRESH_TOKEN": "your_refresh_token"
      }
    }
  }
}

</details>

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Usage Examples

Once connected, just talk to Claude:

• "What's my current Z2 pace and how has it changed over the last 3 months?"
• "My left knee is sore — adjust my training plan for this week"
• "Am I on track for my sub-50 10K goal?"
• "How was my running this month compared to last month?"
• "Which shoes have the most miles on them?"

Memory is stored in ~/.strava-mcp/memory/ as plain JSON files — easy to inspect or back up.

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Security

Credentials are loaded from environment variables only — never hardcoded. The server uses Strava's OAuth2 refresh token flow and automatically handles token renewal.

Requirements

• Python 3.10+
• A Strava account with API access
• Claude Desktop (or any MCP-compatible client)

License

MIT