WHOOP MCP Server for LangChain 11 tools — connect in under 2 minutes
LangChain is the leading Python framework for composable LLM applications. Connect WHOOP through the Vinkius and LangChain agents can call every tool natively — combine them with retrievers, memory, and output parsers for sophisticated AI pipelines.
ASK AI ABOUT THIS MCP SERVER
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({
"whoop": {
"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 WHOOP, 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 WHOOP MCP Server
Connect your WHOOP account to any AI agent and access your personal health data through natural conversation.
LangChain's ecosystem of 500+ components combines seamlessly with WHOOP through native MCP adapters. Connect 11 tools via the 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
- Profile — View your WHOOP profile and body measurements (height, weight, max HR)
- Cycles — Browse your 24-hour WHOOP cycles combining sleep, strain and recovery
- Sleep — Analyze sleep data with stages (light, deep, REM), duration and performance
- Recovery — Track recovery scores, HRV, resting heart rate and sleep balance
- Workouts — Review workout data with strain, heart rate zones, duration and calories
- Pagination — Navigate through historical data with date ranges and pagination tokens
The WHOOP MCP Server exposes 11 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 WHOOP to LangChain via MCP
Follow these steps to integrate the WHOOP 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 11 tools from WHOOP via MCP
Why Use LangChain with the WHOOP MCP Server
LangChain provides unique advantages when paired with WHOOP through the Model Context Protocol.
The largest ecosystem of integrations, chains, and agents — combine WHOOP 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 WHOOP queries for multi-turn workflows
WHOOP + LangChain Use Cases
Practical scenarios where LangChain combined with the WHOOP MCP Server delivers measurable value.
RAG with live data: combine WHOOP tool results with vector store retrievals for answers grounded in both real-time and historical data
Autonomous research agents: LangChain agents query WHOOP, synthesize findings, and generate comprehensive research reports
Multi-tool orchestration: chain WHOOP tools with web scrapers, databases, and calculators in a single agent run
Production monitoring: use LangSmith to trace every WHOOP tool call, measure latency, and optimize your agent's performance
WHOOP MCP Tools for LangChain (11)
These 11 tools become available when you connect WHOOP to LangChain via MCP:
get_body_measurement
Useful for tracking physical metrics alongside your WHOOP data. Get your body measurement data
get_cycle
Includes sleep, recovery, strain and heart rate metrics for that 24-hour period. Get a specific WHOOP cycle by ID
get_cycle_recovery
Includes recovery score, resting heart rate, HRV (heart rate variability), sleep balance and strain balance. Get recovery data for a specific WHOOP cycle
get_cycle_sleep
Includes sleep duration, stages (light, deep, REM, awake), disturbances and sleep performance percentage. Get sleep data for a specific WHOOP cycle
get_cycles
Cycles represent 24-hour periods of recovery and strain data. Each cycle includes sleep, recovery, strain and heart rate metrics. Supports date range filtering with start/end in ISO 8601 format. Pagination: max 25 results, use nextToken for more. Get your WHOOP cycle data
get_profile
Use this to verify your authentication is working and get your user ID for other endpoints. Get your WHOOP profile info
get_recovery
Supports date range filtering. Pagination: max 25 results per request. Get your WHOOP recovery data
get_sleep
Supports date range filtering. Pagination: max 25 results. Get your WHOOP sleep data
get_sleep_by_id
Includes full sleep stages, disturbances, respiratory rate and sleep performance. Get a specific WHOOP sleep record by ID
get_workout
Includes strain score, duration, heart rate zones, calories burned and GPS data if available. Get a specific WHOOP workout by ID
get_workouts
Supports date range filtering. Pagination: max 25 results. Get your WHOOP workout data
Example Prompts for WHOOP in LangChain
Ready-to-use prompts you can give your LangChain agent to start working with WHOOP immediately.
"Show me my recovery score from today."
"How did I sleep last night?"
"Show me my workouts from this week."
Troubleshooting WHOOP MCP Server with LangChain
Common issues when connecting WHOOP to LangChain through the Vinkius, and how to resolve them.
MultiServerMCPClient not found
pip install langchain-mcp-adaptersWHOOP + LangChain FAQ
Common questions about integrating WHOOP 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 WHOOP 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 WHOOP to LangChain
Get your token, paste the configuration, and start using 11 tools in under 2 minutes. No API key management needed.