How to Use the YMovE Fitness MCP in AutoGen

Q: How can AutoGen help me design complex fitness plans with YMovE Fitness?

You set up multiple agents: one generates the program (generateprogram), another checks for safety, and a third ensures compliance. They debate until they agree on a final, reviewed plan.

Q: Can I use YMovE Fitness with AutoGen to find recipes?

Sure. One agent can search by diet (searchrecipes), and a second 'Cuisine Agent' debates which specific recipe details are most suitable based on ingredients, using getrecipedetails.

Get consensus on fitness plans with multi-agent debate using AutoGen and YMovE Fitness.

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Connect YMovE Fitness MCP to AutoGen

Create your Vinkius account to connect YMovE Fitness to AutoGen and route execution through our secure gateway. The platform manages server hosting, runtime updates, and security layers. Configuration requires no manual server provisioning.

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Setup YMovE Fitness with AutoGen

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Perplexity

MCP Server: Debating Program Design

Need a program? Assign one agent to run `generate_program` based on 'hypertrophy' goals. Then, assign a second agent to act as the safety reviewer—it can flag potential risks or imbalances in the generated routine. They debate until the final output is consensus-driven.

Multi-Agent Meal Plan Review

You don't just accept a meal plan. One agent runs `generate_meal_plan` using calorie targets. A second, 'Dietitian Agent,' then reviews the output against specific dietary constraints (like keto) by cross-referencing data from `search_recipes`. They work together to refine it.

MCP Server: Conflict Resolution for Workouts

An agent can run `generate_workout` for today, but what if the user is exhausted? A second 'Performance Agent' reviews that routine against the available muscle groups (`list_muscle_groups`) and suggests swaps or reductions. This debate ensures a practical, executable plan.

Setup guide

Set up YMovE Fitness MCP in AutoGen

Prerequisites

Python 3.10+ installed
autogen-ext[mcp] package
Active Vinkius subscription with a valid endpoint token

1

Install AutoGen with MCP
Run pip install "autogen-ext[mcp]" autogen-agentchat. The MCP extension includes mcp_server_tools for stateless tool access.
2

Fetch tools from the MCP
Call mcp_server_tools(SseServerParams(url=...)) with your Vinkius endpoint. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Run your agent
Pass the tools to AssistantAgent and call agent.run(). The agent invokes YMovE Fitness tools and returns structured results.

agent.py

from autogen_ext.tools.mcp import SseServerParams, mcp_server_tools
from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient

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

tools = await mcp_server_tools(server_params)

agent = AssistantAgent(
    name="YMovE Fitness_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

result = await agent.run("List recent YMovE Fitness data")
print(result.messages[-1].content)

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Prerequisites

Python 3.10+ installed
autogen-ext[mcp] + autogen-agentchat
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Same packages as above. McpWorkbench is ideal when your agent needs stateful sessions across multiple tool calls.
2

Use McpWorkbench as context manager
Wrap your agent in async with McpWorkbench(...) to maintain shared state and resources. The workbench manages the full MCP session lifecycle.
3

Run with workbench
Pass workbench=workbench to your agent. State is preserved across multiple tool calls within the same session.

agent.py

from autogen_ext.tools.mcp import McpWorkbench, SseServerParams
from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient

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

async with McpWorkbench(server_params) as workbench:
    agent = AssistantAgent(
        name="YMovE Fitness_assistant",
        model_client=OpenAIChatCompletionClient(model="gpt-4o"),
        workbench=workbench,
    )

    result = await agent.run("List recent YMovE Fitness data")
    print(result.messages[-1].content)

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 YMovE Fitness. 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 YMovE Fitness MCP in AutoGen

You set up multiple agents: one generates the program (`generate_program`), another checks for safety, and a third ensures compliance. They debate until they agree on a final, reviewed plan.

Yes. You can have agents argue over the best diet approach: one agent suggests `generate_meal_plan`, and another validates it using `search_foods` data to confirm macro targets are met.

You can assign agents to debate the best exercise choice. One runs `search_exercises`, and a second agent uses `get_exercise_details` to verify that the recommended movement is appropriate for the user's current fitness level.

Sure. One agent can search by diet (`search_recipes`), and a second 'Cuisine Agent' debates which specific recipe details are most suitable based on ingredients, using `get_recipe_details`.

The server handles Nutritional Data. This includes UPC/EAN barcode lookups for food items, nutritional breakdowns, and recipe ingredients.

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