How to Use the Konnektive MCP in LangChain

Run multi-step billing updates and customer lookups by chaining Konnektive CRM actions within your LangChain pipeline.

See Vinkius in Action

Works with every AI agent you already use

…and any MCP-compatible client

MCP Servers - Free for Subscribers

Connect Konnektive MCP to LangChain

Create your Vinkius account to connect Konnektive 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.

GDPR Free for Subscribers

Setup Konnektive with LangChain

Ask AI about this MCP

ChatGPT

Claude

Perplexity

Build multi-step Konnektive pipelines in LangChain

This MCP Server exposes `query_konnektive_customers` and `get_customer_details` to let your LangChain agents run multi-step customer lookups. Stop writing manual glue code for your CRM. The agent evaluates the output of each step to decide which tool to call next. This setup shines when you need to handle complex billing issues. Your agent can query transactions using `query_konnektive_transactions` and immediately trigger shipping updates with `update_order_shipping_address` if it detects a failed delivery pattern. Every step of this execution is tracked in LangSmith so you can see exactly how the agent handled the Konnektive data.

Trace transaction audits with LangSmith observability

By connecting this MCP Server to your LangChain setup, `get_konnektive_audit_logs` exposes system history directly to your tracing workflows. Debugging automated billing changes is a nightmare without visibility. You get a clear view of how your agent parsed the JSON filters before sending them to the CRM. This transparency means you can confidently automate compliance checks. When the agent pulls campaigns using `list_billing_campaigns`, you can verify the exact token usage and latency of the call. If an order update fails, you will see the raw error response in your tracing dashboard instantly.

Sync order routing across multiple fulfillment centers

Your LangChain agent uses this MCP Server to query `query_konnektive_orders` and `list_fulfillment_houses` to match active shipments with physical warehouse locations. Keep your inventory and shipping in lockstep. It handles the routing decisions based on real-time data instead of static, outdated rules. If an order needs a destination change, the agent calls `update_order_shipping_address` to fix it before fulfillment begins. You can also pull product details with `list_konnektive_products` to verify stock before updating the order. It turns a tedious manual verification process into a fast, autonomous chain.

Setup guide

Set up Konnektive 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 Konnektive 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({
    "konnektive-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 Konnektive transactions"
    })
    print(result["messages"][-1].content)

Get your connection token →

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

Vinkius connects your tools to AI with real-time monitoring and automatic cost savings — all from one dashboard.

Connect Konnektive now

Real-time monitoring

Live

visibility into every interaction

Connect your favorite tools to your AI and see exactly what's happening — every request, every response, in real time.

Built-in savings

60%

lower AI costs

Vinkius compresses data between your apps and your AI automatically. Lower bills every month — no configuration required.

Single dashboard

One

place for every integration

Every tool your AI connects to, managed from a single screen. One account, complete control.

Common questions about Konnektive MCP in LangChain

Install the adapter package and initialize the MultiServerMCPClient with your Vinkius server URL. Call client.get_tools() and pass those tools directly to your create_agent function.

Yes, by default the connection is stateless, but you can use client.session() to maintain context across multiple tool calls. This allows the agent to remember previous get_customer_details outputs during a long conversation.

The agent automatically formats the JSON filter strings required by query_konnektive_orders and query_konnektive_transactions. It uses the tool's schema definitions to structure the arguments correctly before making the call.

You can run the server via the Vinkius platform or run it locally using the command line. Your agent connects to the provided HTTP endpoint, giving you instant access to all ten CRM tools.

All customer details and transaction histories retrieved via get_customer_details remain inside your execution environment. Vinkius runs the server in an isolated sandbox, meaning your sensitive CRM data is never stored or used to train public models.

Use it with your favorite AI tools

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

OpenAI Agents SDK sdk-python

Google ADK sdk-python

Pydantic AI sdk-python

Vercel AI SDK sdk-typescript