How to Use the SkootEco MCP in AutoGen

Q: How does SkootEco help AutoGen manage carbon emissions?

AutoGen can set up a debate: Agent A runs getemissions, and Agent B challenges it by running getemissionsbycategory. The resulting conversation helps the user reach a consensus on necessary reductions.

Q: Can SkootEco help AutoGen with ESG report generation?

Yes. One agent can call getesgreport to get the draft, and another agent acts as an auditor, running getmetrics against it to ensure all required data points are present in the final consensus.

Q: How does SkootEco handle project data for AutoGen?

Agents can argue over investment. One agent checks listprojects to identify options, and another uses getproject to model the financial impact of selecting a specific resource.

Force a debate on sustainability decisions with AutoGen and your AI client.

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Setup SkootEco with AutoGen

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Conflict-Driven Emission Analysis

Agents can argue over emission levels. Start by calling `get_emissions` to establish the current baseline. Then, one agent can call `list_categories`, while another challenges it using `add_emission`. This forces a debate on which category is the biggest risk.

Strategic Mitigation Debate

Want to decide the best way forward? One agent might check current offsets via `get_offset`, while a second agent proposes planting trees using `plant_tree`. The system then debates whether buying an offset (`purchase_offset`) or local action is superior.

Compliance Goal Setting Consensus

Multiple agents can negotiate goals. One might run `get_impact_profile` to find weaknesses, while a second calls `get_report` for the current status. The final consensus must reconcile these two differing views into an actionable plan.

Setup guide

Set up SkootEco 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 SkootEco 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="SkootEco_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

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

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

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Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by SkootEco. 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 SkootEco MCP in AutoGen

AutoGen can set up a debate: Agent A runs `get_emissions`, and Agent B challenges it by running `get_emissions_by_category`. The resulting conversation helps the user reach a consensus on necessary reductions.

Yes. One agent can call `get_esg_report` to get the draft, and another agent acts as an auditor, running `get_metrics` against it to ensure all required data points are present in the final consensus.

The server supports defining scopes. Agents can debate whether the focus should be on Scope 1 (direct actions) or Scope 3 (supply chain), forcing a deep discussion about reporting boundaries.

Agents can argue over investment. One agent checks `list_projects` to identify options, and another uses `get_project` to model the financial impact of selecting a specific resource.

This MCP Server tracks carbon emissions, which are numerical impact metrics. This includes Scope 1, 2, and 3 activity data points that inform all reports generated by the MCP Server.

Use it with your favorite AI tools

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

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