How to Use the Adversus MCP in LangChain

Q: How do I connect LangChain to Adversus?

Install the adapter with pip install langchain-mcp-adapters langgraph. Then initialize MultiServerMCPClient pointing to your Vinkius endpoint URL. Pass the resulting tools directly into your ReAct agent.

Q: Can I trace Adversus MCP tool calls in LangSmith?

Yes. Every execution of listcampaigns or getcampaigndetails registers as a distinct step. You see the exact JSON inputs and outputs along with latency metrics.

Build sales pipelines that convert. LangChain agents pull live Adversus call data and feed it directly into custom ReAct chains.

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Connect Adversus MCP to LangChain

Create your Vinkius account to connect Adversus 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 Adversus with LangChain

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Chain Adversus MCP Server Data

Your ReAct agent needs live context before making decisions. Calling `list_active_leads` grabs the entire global queue straight from the dialer. The agent parses those records and immediately decides which campaign fits best. Output from that first step feeds directly into `get_campaign_details`. LangChain passes the campaign IDs down the pipeline, checking capacity before moving a single record. Tracing via LangSmith shows exactly how many tokens you spent parsing those lead profiles.

Automate Contact Routing

Moving contacts manually wastes hours of prime calling time. You can write a chain that evaluates incoming project data using `list_crm_projects` and matches it against your current team roster. The script maps out exactly who handles what. Once the logic finishes evaluating, the agent fires `add_contact_to_campaign`. It drops the new prospect right into the active dialing queue via this MCP integration. Your reps just keep talking while the background process keeps their lists full.

Track Rep Performance Metrics

Managers want to know who is assigned to which dialing group. Executing `list_account_users` pulls the current roster of team members active in the system. Your agent cross-references this list with active campaigns to build a complete picture of floor coverage. Combining that roster with `list_campaign_contacts` reveals exactly how many prospects sit in each rep's bucket. You build a daily summary chain that runs at midnight and drops a formatted report into Slack. No more guessing about workload distribution.

Setup guide

Set up Adversus 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 Adversus 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({
    "adversus-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 Adversus 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 Adversus. 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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Real-time monitoring

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Common questions about Adversus MCP in LangChain

Install the adapter with `pip install langchain-mcp-adapters langgraph`. Then initialize `MultiServerMCPClient` pointing to your Vinkius endpoint URL. Pass the resulting tools directly into your ReAct agent.

Yes. Every execution of `list_campaigns` or `get_campaign_details` registers as a distinct step. You see the exact JSON inputs and outputs along with latency metrics.

The chain catches the error and can retry or route to a fallback MCP tool. You configure the retry logic inside LangGraph based on the specific HTTP response.

Vinkius handles the underlying credentials. Your script just needs the single Vinkius endpoint token to authenticate the MCP Server connection.

Contact names and phone numbers remain confined to your V8 Isolate Sandbox during execution. The ephemeral environment destroys the memory state the second your pipeline finishes running. Nothing gets stored permanently on the server side.

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