How to Use the Awattar MCP in AutoGen

Q: How do I register Awattar tools with an AutoGen agent?

You install the autogen-extmcp package and use the mcpservertools helper with your server URL. Pass the resulting tool list directly into your AssistantAgent constructor to give them access.

Q: Can AutoGen agents negotiate energy savings using Awattar prices?

Yes, one agent can fetch live prices via getmarketdata while another calculates the battery discharge rate. They discuss the optimal dispatch window in their conversation thread before running the load.

Q: How do AutoGen agents handle the YAML stats from Awattar?

The agents call getcurrentyaml to get the latest structured pricing statistics. A coder agent can then use this YAML payload to update Home Assistant automation files while a reviewer agent checks the syntax.

Let your AutoGen agents debate and coordinate home energy consumption based on live Awattar EPEX Spot pricing.

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Setup Awattar with AutoGen

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Coordinate Energy Debates in AutoGen

The `get_market_data` tool provides the critical price feed that your AutoGen agents need to negotiate high-load appliance schedules. A budget agent can argue for delaying EV charging until rates drop, while a comfort agent checks if the battery can handle the wait. This MCP setup allows multiple specialized agents to analyze the same EPEX Spot data simultaneously. They debate the trade-offs in a shared conversation history, arriving at a consensus before executing any physical smart home changes.

Validate Home Assistant YAML via Agent Consensus

The `get_current_yaml` tool delivers structured price statistics that your AutoGen developer agent uses to draft automation scripts. Before applying the code, a dedicated QA agent reviews the YAML structure to ensure it matches Home Assistant standards. Using this multi-agent review process prevents broken configurations from entering your live smart home directory. The MCP server feeds the raw YAML data directly to the group chat, where agents verify the syntax before writing to disk.

Resolve Tariff Conflicts with AutoGen Agents

Calling the `get_market_data` tool inside a multi-agent loop helps resolve conflicting operational goals when spot prices spike. Your agents weigh the cost of running a heat pump during peak hours against the potential drop in indoor temperature. This collaborative decision-making process ensures your home runs efficiently without sacrificing basic comfort. The MCP integration supplies the raw pricing numbers, leaving the optimization strategy to the debating agents.

Setup guide

Set up Awattar 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 Awattar 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="Awattar_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

result = await agent.run("List recent Awattar 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="Awattar_assistant",
        model_client=OpenAIChatCompletionClient(model="gpt-4o"),
        workbench=workbench,
    )

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

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Common questions about Awattar MCP in AutoGen

You install the `autogen-ext[mcp]` package and use the `mcp_server_tools` helper with your server URL. Pass the resulting tool list directly into your `AssistantAgent` constructor to give them access.

Yes, one agent can fetch live prices via `get_market_data` while another calculates the battery discharge rate. They discuss the optimal dispatch window in their conversation thread before running the load.

The agents call `get_current_yaml` to get the latest structured pricing statistics. A coder agent can then use this YAML payload to update Home Assistant automation files while a reviewer agent checks the syntax.

The adapter handles both stdio and Streamable HTTP transports out of the box. This makes it easy to connect your multi-agent team to either a local or a hosted server instance.

This MCP server only reads public EPEX Spot electricity rates and formats them into standard YAML blocks. It does not access your local smart home credentials, device IDs, or private energy consumption history during the agent debate.

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