How to Use the CustomerGauge MCP in LangChain

Q: What tools are available for LangChain users?

You get full access to 10 tools including getresponsedetails and searchresponsesbykeyword. Everything is typed for your framework.

Build multi-step NPS reasoning chains for your LangChain agents with direct access to CustomerGauge account data.

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Works with every AI agent you already use

…and any MCP-compatible client

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Connect CustomerGauge MCP to LangChain

Create your Vinkius account to connect CustomerGauge 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 CustomerGauge with LangChain

Ask AI about this MCP

ChatGPT

Claude

Perplexity

Chain NPS metrics in LangChain

Feed `get_account_nps` or `get_portfolio_nps_summary` directly into your agent's execution sequence. You can pipe these results into further processing steps without manual intervention. Your agent decides when to pull data based on the chain's state. It creates a fluid pipeline where satisfaction scores drive subsequent logic.

Automate revenue impact analysis

Connect `list_revenue_impact_data` to your analytical chains to map financial outcomes to customer feedback. This lets you calculate ROI based on real-time survey inputs. Reasoning agents evaluate these links during the execution flow. It turns raw financial figures into actionable insights for your team.

Manage contact data within chains

Use `list_account_contacts` to identify key stakeholders before your agent initiates follow-up tasks. This ensures your pipelines always target the correct individuals. Every contact search feeds back into the chain. You maintain full observability through every step of the interaction.

Setup guide

Set up CustomerGauge 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 CustomerGauge 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({
    "customergauge-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 CustomerGauge 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 CustomerGauge. 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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Connect your favorite tools to your AI and see exactly what's happening — every request, every response, in real time.

Built-in savings

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place for every integration

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

Common questions about CustomerGauge MCP in LangChain

You install the MCP adapters and point your client to the Vinkius endpoint. The agent then discovers and executes tools like `get_business_unit_nps` as needed.

Yes, you use the session client to maintain context across calls. This keeps your state intact between different tool invocations.

All traffic stays encrypted in transit using your unique endpoint token. We handle the auth so your private feedback records never sit in plain text.

You get full access to 10 tools including `get_response_details` and `search_responses_by_keyword`. Everything is typed for your framework.

The server only touches your specific NPS scores and verbatim comments via authorized requests. Your feedback data is never shared outside your private environment.

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