How to Use the Corrently Regional Green Index MCP in AutoGen

Let your AutoGen agents debate grid strategy by using this MCP server to fetch real-time energy market data.

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Connect Corrently Regional Green Index MCP to AutoGen

Create your Vinkius account to connect Corrently Regional Green Index 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 Corrently Regional Green Index with AutoGen

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Debate grid data in AutoGen

Your agents use `get_regional_green_index` to challenge each other's assumptions about energy consumption. One agent might suggest a task start time, while the other critiques it based on the green index. This consensus-driven approach ensures your system doesn't commit to high-energy tasks during dirty grid periods. The negotiation happens in real-time as the agents parse the tool output.

Negotiate with market prices

Use `get_energy_market_data` to force your agents to negotiate costs during their conversation. An agent focused on budget will flag risks when market prices spike above your defined threshold. Your agents compare their internal goals against the live market feed provided by the tool. This negotiation process leads to decisions that balance cost, performance, and green energy goals.

Automate agent tool conversion

The MCP tool adapter automatically converts the server schema for your AutoGen agents. You pass the tool list directly into the constructor, and the agents handle the function calls internally. This keeps your multi-agent system clean and focused on the conversation logic rather than manual plumbing. The agents invoke the tools as needed during their debate cycles.

Setup guide

Set up Corrently Regional Green Index 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 Corrently Regional Green Index 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="Corrently Regional Green Index_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

result = await agent.run("List recent Corrently Regional Green Index 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="Corrently Regional Green Index_assistant",
        model_client=OpenAIChatCompletionClient(model="gpt-4o"),
        workbench=workbench,
    )

    result = await agent.run("List recent Corrently Regional Green Index data")
    print(result.messages[-1].content)

Get your connection token →

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Common questions about Corrently Regional Green Index MCP in AutoGen

Use the provided streamable HTTP parameters to define the server connection. The MCP adapter then maps the tools to your agents, allowing them to call the grid index during debates.

The agents receive the raw data from the server and incorporate it into their decision-making conversation. You can prompt them to treat the index as a critical constraint for their tasks.

The server only handles public energy statistics. No user account data or private identification is exposed during the agent conversation.

The agents pass the data between themselves as part of their chat history. This ensures all agents have the context needed to reach a consensus.

The tools return numerical energy pricing and green index scores. These values are processed as text or numbers within the agent's internal reasoning loop.

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