Strava Planning MCP Server for LangChain 14 tools — connect in under 2 minutes
LangChain is the leading Python framework for composable LLM applications. Connect Strava Planning through Vinkius and LangChain agents can call every tool natively. combine them with retrievers, memory, and output parsers for sophisticated AI pipelines.
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Vinkius supports streamable HTTP and SSE.
import asyncio
from langchain_mcp_adapters.client import MultiServerMCPClient
from langchain_openai import ChatOpenAI
from langgraph.prebuilt import create_react_agent
async def main():
# Your Vinkius token. get it at cloud.vinkius.com
async with MultiServerMCPClient({
"strava-planning": {
"transport": "streamable_http",
"url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp",
}
}) as client:
tools = client.get_tools()
agent = create_react_agent(
ChatOpenAI(model="gpt-4o"),
tools,
)
response = await agent.ainvoke({
"messages": [{
"role": "user",
"content": "Using Strava Planning, show me what tools are available.",
}]
})
print(response["messages"][-1].content)
asyncio.run(main())
* 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 Planning MCP Server
Connect Strava Planning to any AI agent and manage your training logistics — route creation, GPX/TCX export, manual activity logging, gear tracking, segment favoriting, and profile management.
LangChain's ecosystem of 500+ components combines seamlessly with Strava Planning through native MCP adapters. Connect 14 tools via Vinkius and use ReAct agents, Plan-and-Execute strategies, or custom agent architectures. with LangSmith tracing giving full visibility into every tool call, latency, and token cost.
What you can do
- Route Management — List, view, and analyze all your saved routes with distance, elevation, and descriptions
- Route Streams — Get GPS coordinates, elevation profiles, and distance data for any route
- Route Export — Export routes to GPX and TCX formats for GPS devices (Garmin, Wahoo, etc.)
- Manual Activity Creation — Log activities not recorded by Strava (gym, yoga, cross-training) with full details
- Activity Updates — Edit activity names, descriptions, assign gear, mark commutes or indoor sessions
- File Uploads — Upload FIT, TCX, or GPX files for processing by Strava with status tracking
- Segment Management — Star (favorite) or unstar segments for quick training access
- Athlete Profile — View and update your profile information including weight for accurate power-to-weight ratios
- Athlete Zones — Review your heart rate and power zone configurations
- Gear Details — Track equipment mileage, models, and primary gear assignments
The Strava Planning MCP Server exposes 14 tools through the Vinkius. Connect it to LangChain 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 Planning to LangChain via MCP
Follow these steps to integrate the Strava Planning MCP Server with LangChain.
Install dependencies
Run pip install langchain langchain-mcp-adapters langgraph langchain-openai
Replace the token
Replace [YOUR_TOKEN_HERE] with your Vinkius token
Run the agent
Save the code and run python agent.py
Explore tools
The agent discovers 14 tools from Strava Planning via MCP
Why Use LangChain with the Strava Planning MCP Server
LangChain provides unique advantages when paired with Strava Planning through the Model Context Protocol.
The largest ecosystem of integrations, chains, and agents. combine Strava Planning MCP tools with 500+ LangChain components
Agent architecture supports ReAct, Plan-and-Execute, and custom strategies with full MCP tool access at every step
LangSmith tracing gives you complete visibility into tool calls, latencies, and token usage for production debugging
Memory and conversation persistence let agents maintain context across Strava Planning queries for multi-turn workflows
Strava Planning + LangChain Use Cases
Practical scenarios where LangChain combined with the Strava Planning MCP Server delivers measurable value.
RAG with live data: combine Strava Planning tool results with vector store retrievals for answers grounded in both real-time and historical data
Autonomous research agents: LangChain agents query Strava Planning, synthesize findings, and generate comprehensive research reports
Multi-tool orchestration: chain Strava Planning tools with web scrapers, databases, and calculators in a single agent run
Production monitoring: use LangSmith to trace every Strava Planning tool call, measure latency, and optimize your agent's performance
Strava Planning MCP Tools for LangChain (14)
These 14 tools become available when you connect Strava Planning to LangChain via MCP:
create_activity
Required: name (activity name), type (activity type like "Run", "Ride", "Swim", "Walk", "Hike"), startDate (ISO 8601 format), elapsedTime (seconds). Optional: description, distance (meters). Use this to log activities recorded outside of Strava (gym workouts, yoga, cross-training, etc.). Activity types must match Strava's valid types list. Create a manual activity in Strava
export_route_gpx
GPX files can be downloaded and loaded onto GPS devices (Garmin, Wahoo, etc.) for navigation. The routeId is from Strava route URLs. Use this to export routes to your GPS device for guided training. Get the GPX export URL for a Strava route
export_route_tcx
TCX files include route data with additional training metadata. Compatible with Garmin Training Center and other fitness platforms. Use this to export routes with training metadata. Get the TCX export URL for a Strava route
get_athlete
Use this to review personal profile details, check equipment assignments, or verify account settings. Get the authenticated athlete's profile information
get_athlete_zones
Required for zone-based training analysis. Use this to review training zones, ensure zones are correctly set, or use zone data for activity analysis. Get the athlete's custom heart rate and power zones
get_gear
The gearId is found in activity data or athlete profile. Use this to check equipment mileage for maintenance planning or to analyze performance with specific gear. Get details about a piece of equipment (bike, shoes) tracked in Strava
get_route
The routeId is found in Strava route URLs. Use this to review route characteristics before training or to plan similar routes. Get detailed information about a specific Strava route
get_route_streams
The "types" parameter is comma-separated: "latlng", "altitude", "distance". Use this to preview a route's elevation profile, understand the terrain, or export GPS data for navigation. Get elevation and GPS data streams for a Strava route
get_upload_status
Status values: "Your activity is ready" (success), "Your activity is still processing" (wait and retry), or error messages. The uploadId is returned by upload_activity. Poll this endpoint every 5-10 seconds after upload until ready. Check the status of a Strava activity upload
list_routes
Each route includes: name, distance, elevation gain, type (ride/run), description, and whether it's private. Use this to review saved routes, plan upcoming workouts, or export route data for GPS devices. List all routes created by the authenticated athlete
star_segment
Set starred=true to favorite, starred=false to unfavorite. The segmentId is from Strava segment URLs. Use this to manage your favorite segments for quick access and training focus. Star (favorite) or unstar a Strava segment
update_activity
The activityId is the numeric ID. Updatable fields: name, description, sport_type, gear_id (to assign equipment), commute (mark as commute: "true"/"false"), trainer (mark as indoor: "true"/"false"). Use this to correct activity details, assign gear, or add descriptions after the fact. Update an existing Strava activity
update_athlete
Currently only "weight" (in kg) is supported by the API. Accurate weight is important for power-to-weight ratio calculations and performance analysis. Use this when your weight changes to keep performance metrics accurate. Update the authenticated athlete's profile information
upload_activity
Supported data_type: "fit", "fit.gz", "tcx", "tcx.gz", "gpx", "gpx.gz". Returns an upload ID to check status with get_upload_status. Note: Actual file upload requires multipart/form-data with the file content. This endpoint initiates the process. Check upload status periodically — processing takes 10-60 seconds. Upload an activity file (FIT, TCX, GPX) to Strava for processing
Example Prompts for Strava Planning in LangChain
Ready-to-use prompts you can give your LangChain agent to start working with Strava Planning immediately.
"List all my saved routes."
"Export route 12345 to GPX format."
"Create a manual activity for today's gym session."
Troubleshooting Strava Planning MCP Server with LangChain
Common issues when connecting Strava Planning to LangChain through the Vinkius, and how to resolve them.
MultiServerMCPClient not found
pip install langchain-mcp-adaptersStrava Planning + LangChain FAQ
Common questions about integrating Strava Planning MCP Server with LangChain.
How does LangChain connect to MCP servers?
langchain-mcp-adapters to create an MCP client. LangChain discovers all tools and wraps them as native LangChain tools compatible with any agent type.Which LangChain agent types work with MCP?
Can I trace MCP tool calls in LangSmith?
Connect Strava Planning with your favorite client
Step-by-step setup guides for every MCP-compatible client and framework:
Anthropic's native desktop app for Claude with built-in MCP support.
AI-first code editor with integrated LLM-powered coding assistance.
GitHub Copilot in VS Code with Agent mode and MCP support.
Purpose-built IDE for agentic AI coding workflows.
Autonomous AI coding agent that runs inside VS Code.
Anthropic's agentic CLI for terminal-first development.
Python SDK for building production-grade OpenAI agent workflows.
Google's framework for building production AI agents.
Type-safe agent development for Python with first-class MCP support.
TypeScript toolkit for building AI-powered web applications.
TypeScript-native agent framework for modern web stacks.
Python framework for orchestrating collaborative AI agent crews.
Leading Python framework for composable LLM applications.
Data-aware AI agent framework for structured and unstructured sources.
Microsoft's framework for multi-agent collaborative conversations.
Connect Strava Planning to LangChain
Get your token, paste the configuration, and start using 14 tools in under 2 minutes. No API key management needed.
