How to Use the Metabolic Energy Estimator MCP in AutoGen

Coordinate fitness agents in AutoGen using the Metabolic Energy Estimator MCP Server.

See Vinkius in Action

Works with every AI agent you already use

…and any MCP-compatible client

MCP Servers - Free for Subscribers

Connect Metabolic Energy Estimator MCP to AutoGen

Create your Vinkius account to connect Metabolic Energy Estimator 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.

GDPR Free for Subscribers

Setup Metabolic Energy Estimator with AutoGen

Ask AI about this MCP

ChatGPT

Claude

Perplexity

Debate metabolic math in AutoGen

Assign one agent to run `calculate_tdee` while another critiques the caloric deficit plan from `calculate_weight_loss_projection`. They discuss the numbers until they reach a consensus. This multi-agent setup ensures that the math is checked twice. It adds a layer of deliberation to your fitness projections.

Negotiate activity burns with agents

Use `search_activity_catalog` to give your agents a common reference for activity intensities. When an agent calculates calories, the others can verify the MET value against the catalog. It prevents agents from drifting into inaccurate estimates. They debate the correct activity ID before calling `estimate_calories_burned`.

Consensus-driven health planning

The `calculate_weight_loss_projection` tool gives your AutoGen agents the raw data they need to build a plan. They work together to ensure the timeline matches the user's goals. They challenge each other's assumptions about weight loss speed. The result is a plan that multiple agents have reviewed and agreed upon.

Setup guide

Set up Metabolic Energy Estimator 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 Metabolic Energy Estimator 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="Metabolic Energy Estimator_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

result = await agent.run("List recent Metabolic Energy Estimator data")
print(result.messages[-1].content)

Get your connection token →

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="Metabolic Energy Estimator_assistant",
        model_client=OpenAIChatCompletionClient(model="gpt-4o"),
        workbench=workbench,
    )

    result = await agent.run("List recent Metabolic Energy Estimator 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 calorie-burn-estimator. 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.

Why Choose Vinkius

Vinkius connects your tools to AI with real-time monitoring and automatic cost savings — all from one dashboard.

Connect Metabolic Energy Estimator now

Real-time monitoring

Live

visibility into every interaction

Connect your favorite tools to your AI and see exactly what's happening — every request, every response, in real time.

Built-in savings

60%

lower AI costs

Vinkius compresses data between your apps and your AI automatically. Lower bills every month — no configuration required.

Single dashboard

One

place for every integration

Every tool your AI connects to, managed from a single screen. One account, complete control.

Common questions about Metabolic Energy Estimator MCP in AutoGen

Use the MCP adapter to register the server tools. You then pass the tool list to your AssistantAgent instances, enabling them to call the math functions as needed.

Yes. The server handles concurrent calls, so your agents can query the catalog or perform calculations simultaneously during their debate.

It provides the deterministic data required for consensus. Because the tools rely on standard formulas, the agents have a shared source of truth to debate from.

If a tool call fails, the agents see the error in the chat history. They can then discuss the failure and decide whether to re-attempt the calculation with different inputs.

The server processes weight inputs in a private, ephemeral sandbox. The agents only see the output of the calculations, keeping your raw health data contained.

Use it with your favorite AI tools

Connect this server to Cursor, Claude, VS Code, and more.

OpenAI Agents SDK sdk-python

Google ADK sdk-python

Pydantic AI sdk-python

Vercel AI SDK sdk-typescript