What data streams are available for activities?

Available streams include: time, distance, latlng (GPS coordinates), altitude, velocity_smooth (speed), heartrate, cadence, watts (power), temp (temperature), moving (moving flag), and grade_smooth (incline %). Request specific streams with comma-separated types, e.g., "heartrate,watts,velocity_smooth". Not all streams are available for every activity — it depends on the recording device.

What are activity zones and how are they calculated?

Activity zones show the time spent in each heart rate zone (Z1-Z5) or power zone during an activity. Zones are based on the athlete's personal zone configuration (set in Strava settings). Zone analysis reveals training intensity — whether a workout was aerobic (Z1-Z2), threshold (Z3), or anaerobic (Z4-Z5). This data requires a heart rate monitor or power meter.

How do I find my athlete ID on Strava?

Your athlete ID is the numeric portion of your Strava profile URL. For example, from https://www.strava.com/athletes/12345678, your athlete ID is 12345678. You can also get it from the get_athlete tool in the Strava Planning or Strava Social MCP servers.

Strava Training MCP Server for Pydantic AI 12 tools — connect in under 2 minutes

Name: Strava Training
Availability: InStock
Author: Vinkius

ai.pydantic.dev/mcp

Built by Vinkius GDPR 12 Tools SDK

Pydantic AI brings type-safe agent development to Python with first-class MCP support. Connect Strava Training through Vinkius and every tool is automatically validated against Pydantic schemas. catch errors at build time, not in production.

Get MCP Server for AI Agents

ASK AI ABOUT THIS MCP SERVER

Open in ChatGPT Open in Claude Open in Perplexity

Vinkius supports streamable HTTP and SSE.

python

import asyncio
from pydantic_ai import Agent
from pydantic_ai.mcp import MCPServerHTTP

async def main():
    # Your Vinkius token. get it at cloud.vinkius.com
    server = MCPServerHTTP(url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")

    agent = Agent(
        model="openai:gpt-4o",
        mcp_servers=[server],
        system_prompt=(
            "You are an assistant with access to Strava Training "
            "(12 tools)."
        ),
    )

    result = await agent.run(
        "What tools are available in Strava Training?"
    )
    print(result.data)

asyncio.run(main())

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* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure

About Strava Training MCP Server

Connect Strava Training to any AI agent and unlock deep performance analysis from your Strava data — activity details, time-series streams, heart rate/power zones, segment efforts, lap splits, and lifetime athlete statistics.

Pydantic AI validates every Strava Training tool response against typed schemas, catching data inconsistencies at build time. Connect 12 tools through Vinkius and switch between OpenAI, Anthropic, or Gemini without changing your integration code. full type safety, structured output guarantees, and dependency injection for testable agents.

What you can do

Activity Details — Full metrics: distance, time, elevation, HR, power, speed, weather, gear
Activity Streams — Raw GPS, heart rate, power, cadence, altitude, speed, temperature, grade data
Activity Zones — Heart rate and power zone distribution for training intensity analysis
Activity Laps — Auto-split lap data with pace, distance, and elevation per split
Segment Efforts — Find, compare, and analyze all efforts on any segment with detailed metrics
Segment Streams — Elevation and grade profiles along segments for previewing difficulty
Segment Details — Distance, elevation, grade, effort count, and personal records
Athlete Statistics — Lifetime and recent totals for runs, rides, and all activities
Athlete Zones — Personal heart rate and power zone configurations
Gear Tracking — Equipment mileage, models, and primary gear assignments

The Strava Training MCP Server exposes 12 tools through the Vinkius. Connect it to Pydantic AI in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.

How to Connect Strava Training to Pydantic AI via MCP

Follow these steps to integrate the Strava Training MCP Server with Pydantic AI.

Install Pydantic AI

Run pip install pydantic-ai

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token

Run the agent

Save to agent.py and run: python agent.py

Explore tools

The agent discovers 12 tools from Strava Training with type-safe schemas

Why Use Pydantic AI with the Strava Training MCP Server

Pydantic AI provides unique advantages when paired with Strava Training through the Model Context Protocol.

Full type safety: every MCP tool response is validated against Pydantic models, catching data inconsistencies before they reach your application

Model-agnostic architecture. switch between OpenAI, Anthropic, or Gemini without changing your Strava Training integration code

Structured output guarantee: Pydantic AI ensures tool results conform to defined schemas, eliminating runtime type errors

Dependency injection system cleanly separates your Strava Training connection logic from agent behavior for testable, maintainable code

Strava Training + Pydantic AI Use Cases

Practical scenarios where Pydantic AI combined with the Strava Training MCP Server delivers measurable value.

Type-safe data pipelines: query Strava Training with guaranteed response schemas, feeding validated data into downstream processing

API orchestration: chain multiple Strava Training tool calls with Pydantic validation at each step to ensure data integrity end-to-end

Production monitoring: build validated alert agents that query Strava Training and output structured, schema-compliant notifications

Testing and QA: use Pydantic AI's dependency injection to mock Strava Training responses and write comprehensive agent tests

Strava Training MCP Tools for Pydantic AI (12)

These 12 tools become available when you connect Strava Training to Pydantic AI via MCP:

get_activity

The activityId is the numeric ID from Strava activity URLs (e.g., strava.com/activities/12345678 → 12345678). Use this for deep analysis of any workout, ride, or run. Get detailed information about a specific Strava activity

get_activity_laps

Each lap includes distance, moving time, average speed, elevation gain, and pace. GPS devices and Strava auto-split activities into laps (typically ~1km or ~1mi). Use this to analyze pace consistency, identify fast/slow sections, and compare splits within a single activity. Get lap/split data for a Strava activity

get_activity_streams

The "types" parameter is comma-separated stream types: "time", "distance", "latlng", "altitude", "velocity_smooth", "heartrate", "cadence", "watts", "temp", "moving", "grade_smooth". Example: "heartrate,watts,velocity_smooth" for HR, power, and speed data. Each stream returns an array of values with corresponding timestamps. Use this for detailed performance analysis, visualization, or export. Get raw time-series data streams from a Strava activity (GPS, heart rate, power, cadence, altitude, speed, etc)

get_activity_zones

Requires activity ID. This data helps understand training intensity and whether the workout targeted the correct zones. Only available for activities with heart rate or power data. Summit/subscription feature. Get heart rate and power zone distribution for a Strava activity

get_athlete_stats

Use the athlete's Strava numeric ID. Returns recent_run_totals, recent_ride_totals, all_run_totals, all_ride_totals. Great for performance overview and progress tracking. Get consolidated activity statistics for any Strava athlete

get_athlete_zones

Requires profile:read_all scope. Use this to understand training zones for zone-based analysis of activities and efforts. Get the authenticated athlete's custom heart rate and power zones

get_gear

The gear ID is found in activity data or athlete profile. Use this to track equipment mileage, plan maintenance, or analyze performance with specific gear. Get details about a piece of equipment (bike, shoes) tracked in Strava

get_segment

The segment ID is found in Strava segment URLs. Use this to discover segment characteristics before attempting it or to compare segments. Get details of a Strava segment including distance, elevation, grade, and leaderboards

get_segment_effort

Includes elapsed time, distance, average speed, heart rate, power, start date, and activity reference. The effort ID is found in segment effort listings or activity details. Use this to analyze specific KOM/QOM attempts and compare efforts on the same segment. Get details of a specific segment effort (KOM/QOM/PR attempt)

get_segment_effort_streams

Same format as activity streams but limited to the segment portion. The "types" parameter is comma-separated: "time", "distance", "latlng", "altitude", "velocity_smooth", "heartrate", "cadence", "watts". Use this for granular analysis of segment performance. Get time-series data streams for a specific segment effort

get_segment_streams

Useful for previewing a segment's difficulty profile before attempting it. The "types" parameter accepts "distance", "altitude", "grade_smooth". Use this to understand elevation changes and steepness patterns along a segment. Get time-series data streams for a Strava segment (elevation profile, grade, etc)

list_segment_efforts

Filter by athlete_id (required), optionally segment_id to get efforts on a specific segment, and date range with start_date_local and end_date_local (ISO 8601 format). Use this to find PRs, analyze progress on segments over time, or compare multiple efforts on the same segment. List all segment efforts for an athlete, optionally filtered by segment and date range

Example Prompts for Strava Training in Pydantic AI

Ready-to-use prompts you can give your Pydantic AI agent to start working with Strava Training immediately.

"Show my athlete statistics."

"Get activity streams for activity 12345678 with heart rate, power, and speed."

"Show my segment efforts on segment 22978."

Troubleshooting Strava Training MCP Server with Pydantic AI

Common issues when connecting Strava Training to Pydantic AI through the Vinkius, and how to resolve them.

MCPServerHTTP not found

Update: pip install --upgrade pydantic-ai

Strava Training + Pydantic AI FAQ

Common questions about integrating Strava Training MCP Server with Pydantic AI.

How does Pydantic AI discover MCP tools?

Create an MCPServerHTTP instance with the server URL. Pydantic AI connects, discovers all tools, and generates typed Python interfaces automatically.

Does Pydantic AI validate MCP tool responses?

Yes. When you define result types as Pydantic models, every tool response is validated against the schema. Invalid data raises a clear error instead of silently corrupting your pipeline.

Can I switch LLM providers without changing MCP code?

Absolutely. Pydantic AI abstracts the model layer. your Strava Training MCP integration works identically with OpenAI, Anthropic, Google, or any supported provider.