How to Use the WHOOP MCP in CrewAI

Q: How can I compare multiple WHOOP recovery points using CrewAI?

You assign one agent to run getrecovery over a date range, and another agent to process the results. The crew structure ensures that all 25 available data points are collected systematically.

Q: What is the best way to get workout history with CrewAI?

Use getworkouts within your operational pipeline. You can filter by date range, and multiple agents will work together to consolidate strain scores and GPS data into one final output.

Q: If I need a full sleep breakdown, which tool should my CrewAI agents use?

The getsleepbyid tool is best. It gives specific data points like respiratory rate and performance percentages for a single record ID.

Q: How do I ensure WHOOP workout details are accurate with CrewAI?

You instruct the agent to use getworkout, providing the precise ID. This guarantees access to strain, heart rate zones, and calories burned for that single recorded event.

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Setup WHOOP with CrewAI

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Perplexity

Coordinated retrieval of WHOOP metrics

An agent can use `get_cycles` to fetch a broad dataset, while another agent uses `get_body_measurement` to pull current physical data. The crew collaborates to analyze both sets of information. The shared memory allows one specialized agent (e.g., the Researcher) to find IDs, and pass those IDs directly to an Action Agent for detailed metric retrieval.

Autonomous WHOOP sleep analysis

A dedicated Sleep Analyst agent uses `get_sleep` or `get_sleep_by_id`. The process is autonomous: the agent researches the history, and a second agent analyzes the performance percentage. The CrewAI framework manages this sequence. It ensures that when one tool finishes gathering data—like sleep disturbances—the next agent can immediately begin processing it.

Analyzing WHOOP recovery status via MCP Server

You assign the Recovery Specialist role to use `get_cycle_recovery`. This agent checks HRV, resting heart rate, and strain balance. The resulting report is automatically passed to a Moderator Agent for summarization. The multi-agent setup means you don't just call a function; you build an entire operational pipeline that executes the WHOOP data analysis.

Setup guide

Set up WHOOP MCP in CrewAI

Prerequisites

Python 3.10+ installed
crewai package (pip install crewai)
Active Vinkius subscription with a valid endpoint token

1

Install CrewAI
Run pip install crewai to install the framework. MCP support is built-in via the mcps parameter.
2

Add the MCP URL to your agent
Pass your Vinkius endpoint directly to the mcps list. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. CrewAI handles tool discovery and caching automatically.
3

Kick off your crew
Create a Crew with your agent and tasks. Call crew.kickoff() — the agent will automatically invoke WHOOP tools as needed.

crew.py

from crewai import Agent, Task, Crew

agent = Agent(
    role="WHOOP Analyst",
    goal="Access and analyze WHOOP data via MCP.",
    backstory="Expert analyst with direct WHOOP access.",
    mcps=[
        "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
    ],
)

task = Task(
    description="List recent WHOOP transactions",
    agent=agent,
    expected_output="A summary of recent activity",
)

crew = Crew(agents=[agent], tasks=[task])
result = crew.kickoff()
print(result)

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Prerequisites

Python 3.10+ installed
crewai + crewai-tools packages
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install crewai crewai-tools. The MCPServerAdapter handles lifecycle management and tool conversion.
2

Connect with MCPServerAdapter
Use MCPServerAdapter as a context manager with SseServerParameters pointing to your Vinkius endpoint. The adapter automatically manages connection lifecycle.
3

Assign tools and run
Pass the returned mcp_tools to your agent's tools parameter. The adapter converts MCP tools to native BaseTool objects compatible with all CrewAI agents.

crew.py

from crewai import Agent, Task, Crew
from crewai_tools import MCPServerAdapter
from mcp import SseServerParameters

server_params = SseServerParameters(
    url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)

with MCPServerAdapter(server_params) as mcp_tools:
    agent = Agent(
        role="WHOOP Analyst",
        goal="Access and analyze WHOOP data via MCP.",
        backstory="Expert analyst with direct WHOOP access.",
        tools=mcp_tools,
    )

    task = Task(
        description="List recent WHOOP transactions",
        agent=agent,
        expected_output="A summary of recent activity",
    )

    crew = Crew(agents=[agent], tasks=[task])
    result = crew.kickoff()
    print(result)

Get your connection token →

Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by WHOOP. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.

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Common questions about WHOOP MCP in CrewAI

You assign one agent to run `get_recovery` over a date range, and another agent to process the results. The crew structure ensures that all 25 available data points are collected systematically.

Use `get_workouts` within your operational pipeline. You can filter by date range, and multiple agents will work together to consolidate strain scores and GPS data into one final output.

Yes. This MCP Server touches body measurement data. The system allows you to filter tools selectively, ensuring that only the necessary metrics are exposed during the agent's operation.

The `get_sleep_by_id` tool is best. It gives specific data points like respiratory rate and performance percentages for a single record ID.

You instruct the agent to use `get_workout`, providing the precise ID. This guarantees access to strain, heart rate zones, and calories burned for that single recorded event.

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