How to Use the Cloverly MCP in LangChain

Q: Can LangChain verify my Cloverly impact?

Yes. Your agent calls getimpactsummary or getimpactbytype to aggregate data from your account. It then uses this information to validate the efficacy of your offset program.

Chain carbon offset actions directly into your LangChain pipelines for automated, verified environmental impact tracking.

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Connect Cloverly MCP to LangChain

Create your Vinkius account to connect Cloverly to LangChain 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 Cloverly with LangChain

Ask AI about this MCP

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Claude

Perplexity

LangChain carbon estimation workflows

Build agentic chains that trigger `create_estimate` the moment a transaction completes. Your agent handles the logic, passing shipment details into the tool to calculate real-time emissions data. The output feeds directly into your next chain link. Use `get_estimate` to pull specific values for downstream processing or reporting tasks.

Automated offset purchasing logic

Program your LangChain agent to execute `create_purchase` based on predefined impact thresholds. It evaluates the data returned from previous steps before committing to a transaction. Verify every action using `get_purchase` to ensure your logs capture the exact credit bought. This creates a closed loop for your sustainability operations.

Managing Cloverly project portfolios

Query the current list of available environmental initiatives with `list_projects`. Your agent decides which projects match your company's internal criteria for carbon removal. Use `get_project` to fetch technical details for any specific initiative. This gives your agent the context needed to make informed, data-backed decisions within the chain.

Setup guide

Set up Cloverly 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 Cloverly 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({
    "cloverly-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 Cloverly transactions"
    })
    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 Cloverly. 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

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Common questions about Cloverly MCP in LangChain

Install the langchain-mcp-adapters package and initialize the MultiServerMCPClient. Point it at your Vinkius endpoint to start pulling tools directly into your agent's execution context.

Yes. Your agent calls `get_impact_summary` or `get_impact_by_type` to aggregate data from your account. It then uses this information to validate the efficacy of your offset program.

LangSmith tracks every call made to this MCP server. You see the latency and the raw JSON inputs passed to each tool in real-time.

The connection is stateless by default. Use a client session if you need to keep context across multiple chain steps.

We only process carbon-related metadata required to generate estimates or purchase credits. All transmissions are encrypted over TLS, and your account credentials never leave the secure Vinkius environment.

Use it with your favorite AI tools

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