How to Use the Zenvia Conversion MCP in LangChain

Q: Does Zenvia Conversion support automated lead creation with LangChain?

Absolutely. You use the createlead tool, feeding it contact details and an initial comment right from your chain's logic. It's a direct action within your multi-step workflow.

Build multi-step Zenvia Conversion workflows with LangChain.

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Connect Zenvia Conversion MCP to LangChain

Create your Vinkius account to connect Zenvia Conversion 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 Zenvia Conversion with LangChain

Ask AI about this MCP

ChatGPT

Claude

Perplexity

Automate Prospect Discovery

Start by using `list_prospects` to gather a full list of contacts in Zenvia Conversion. Then, you can pipe that output directly into `get_prospect` to pull detailed information on specific accounts. This sequence lets your agent figure out who the right target is before moving on. It's ideal for complex sales pipelines where multiple pieces of data need gathering.

Manage Messaging Chains

Send a message to an individual prospect using `send_message` after you first retrieved their details with `get_prospect`. The agent can check the account status with `get_account` before sending anything. You can also use `send_template` for standardized outreach, but the chain ensures you only hit those prospects who meet certain criteria defined by other tools like `list_labels`.

Build Agent Reasoning Pipelines

Your agent can check which agents exist in your account using `list_agents`. After listing them, it might use that list to determine the correct group ID for a subsequent call like `list_groups`. This allows you to build multi-step reasoning where the decision of which tool runs next is based entirely on the results of the previous one. It’s true workflow automation.

Setup guide

Set up Zenvia Conversion 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 Zenvia Conversion 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({
    "zenvia-conversion-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 Zenvia Conversion 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 Zenvia Conversion. 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 Zenvia Conversion MCP in LangChain

LangChain uses the MCP Server to execute specific actions like creating leads or listing prospects. Your AI client calls a tool, and LangChain handles stitching that result into the next logical step of your overall process.

You'll pull back contact details, full prospect records from `get_prospect`, and historical communication logs via `list_interactions`. The output is structured data ready for your next step in the chain.

Yes. Because it's a framework for complex chains, you can build workflows that aggregate calls to both `list_prospects` and `send_message`. You get full observability into the entire sequence.

Absolutely. You use the `create_lead` tool, feeding it contact details and an initial comment right from your chain's logic. It's a direct action within your multi-step workflow.

The server handles structured communication data, including contact details and message content from `send_message`. Always ensure your chain logic validates the recipient's status before sending any information.

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