How to Use the SkootEco MCP in LangChain

Q: How does SkootEco help LangChain manage carbon emissions?

You use the getemissions tool to pull total data. LangChain can then pass this raw number into a subsequent chain that runs calculations against compliance rules, making the process entirely observable.

Q: Can SkootEco help LlamaIndex with ESG report generation?

The getesgreport tool generates the full document. LangChain can take this structured output and feed it into a dedicated agent that processes and structures the data for subsequent steps.

Q: What is the scope of SkootEco's carbon tracking when using LangChain?

The system supports multiple scopes. You call getemissionsbyscope to get precise Scope 1, 2, and 3 data points. This granularity lets your agent make highly informed decisions.

Q: How does SkootEco handle project data for LangChain?

Use listprojects to see available options. The chain can then pass the specific project ID into getproject, allowing subsequent steps in your workflow to analyze that resource's details.

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Connect SkootEco MCP to LangChain

Create your Vinkius account to connect SkootEco to LangChain — we handle the hosting, security, and runtime updates so you don't have to. No server setup required.

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Setup SkootEco with LangChain

Ask AI about this MCP

ChatGPT

Claude

Perplexity Vinkius runs on Gemini

Gemini

Grok

Automated Emissions Tracking Chains

You can get total emissions data by calling `get_emissions`. This output feeds directly into a chain that uses `list_categories` to break down the sources. The agent then knows exactly which categories need attention for compliance.

Compliance Reporting Pipelines

Generating an ESG report is straightforward with `get_esg_report`. You'll want to chain this output into a system that runs `get_impact_profile` immediately afterward. This allows the agent to compare required reporting metrics against real-time operational impact.

Mitigation Action Sequences

Need to balance emissions? First, log an increase using `add_emission`. Then, chain that data into a decision loop calling `get_offset` and finally executing the purchase with `purchase_offset`. The entire sequence runs as one traceable action.

Setup guide

Set up SkootEco MCP in LangChain

Prerequisites

Python 3.10+ installed
langchain-mcp-adapters + langgraph packages
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install langchain-mcp-adapters langgraph langchain-openai. The MCP adapters package converts MCP tools into native LangChain BaseTool objects.
2

Connect via HTTP transport
Use MultiServerMCPClient with "transport": "http" pointing to your Vinkius endpoint. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Create a ReAct agent
Pass the discovered tools to create_react_agent() from LangGraph. The agent automatically routes SkootEco tool calls through the MCP protocol.
4

Run with any LLM
Swap ChatOpenAI for ChatAnthropic, ChatGoogleGenerativeAI, or any LangChain-compatible model. The MCP tools work identically across all providers.

agent.py

from langchain_mcp_adapters.client import MultiServerMCPClient
from langgraph.prebuilt import create_react_agent
from langchain_openai import ChatOpenAI

async with MultiServerMCPClient({
    "skooteco-mcp": {
        "transport": "http",
        "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp",
    }
}) as client:
    tools = client.get_tools()

    agent = create_react_agent(
        ChatOpenAI(model="gpt-4o"),
        tools,
    )
    result = await agent.ainvoke({
        "messages": "List recent SkootEco transactions"
    })
    print(result["messages"][-1].content)

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Common questions about SkootEco MCP in LangChain

You use the `get_emissions` tool to pull total data. LangChain can then pass this raw number into a subsequent chain that runs calculations against compliance rules, making the process entirely observable.

The `get_esg_report` tool generates the full document. LangChain can take this structured output and feed it into a dedicated agent that processes and structures the data for subsequent steps.

The system supports multiple scopes. You call `get_emissions_by_scope` to get precise Scope 1, 2, and 3 data points. This granularity lets your agent make highly informed decisions.

Use `list_projects` to see available options. The chain can then pass the specific project ID into `get_project`, allowing subsequent steps in your workflow to analyze that resource's details.

This MCP Server tracks carbon emissions, which are numerical impact metrics. This includes Scope 1, 2, and 3 activity data points.

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