How to Use the CustomerGauge MCP in LlamaIndex

Q: Can LlamaIndex query CustomerGauge history?

Absolutely, as long as you have indexed the output of listsurveyresponses. You can then perform semantic searches across your feedback.

Q: Does CustomerGauge protect privacy in LlamaIndex?

We enforce strict access controls on every getcontactprofile call. Your sensitive survey metadata stays private within your configured environment.

Index your CustomerGauge survey feedback directly into your LlamaIndex knowledge base for semantic search.

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Connect CustomerGauge MCP to LlamaIndex

Create your Vinkius account to connect CustomerGauge to LlamaIndex 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 LlamaIndex

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Index NPS data with LlamaIndex

Run `list_survey_responses` and feed the output into your vector store. This turns static survey results into a searchable knowledge set. Your RAG application retrieves these responses during query time. It grounds your agent's answers in actual customer feedback.

Search feedback by keyword

Use `search_responses_by_keyword` to find specific comments across your entire history. LlamaIndex then parses these results into your index. You ask natural language questions about customer sentiment. The system finds the relevant survey comments instantly.

Unified account profile indexing

Pull `get_contact_profile` data to augment your customer records. LlamaIndex stores this history alongside your existing documentation. Your agent queries this combined knowledge base. It provides accurate, context-aware responses regarding individual customer history.

Setup guide

Set up CustomerGauge MCP in LlamaIndex

Prerequisites

Python 3.10+ installed
llama-index-tools-mcp package
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai. The MCP tools package provides BasicMCPClient and McpToolSpec.
2

Connect with BasicMCPClient
Point BasicMCPClient to your Vinkius endpoint URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. Supports SSE and Streamable HTTP transports.
3

Convert to LlamaIndex tools
Call mcp_tool_spec.to_tool_list_async() to convert all CustomerGauge MCP tools into native FunctionTool objects that any LlamaIndex agent can use.
4

Run with any LLM
Create a FunctionAgent with the tools and your preferred LLM. Swap OpenAI for Anthropic, Gemini, or any LlamaIndex-supported provider.

agent.py

from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI

# Connect to the MCP
mcp_client = BasicMCPClient(
    "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)
mcp_tool_spec = McpToolSpec(client=mcp_client)

# Convert MCP tools to LlamaIndex tools
tools = await mcp_tool_spec.to_tool_list_async()

# Create and run the agent
agent = FunctionAgent(
    tools=tools,
    llm=OpenAI(model="gpt-4o"),
    system_prompt="You have access to CustomerGauge tools.",
)
response = await agent.run("List recent CustomerGauge data")

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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.

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Common questions about CustomerGauge MCP in LlamaIndex

You use the McpToolSpec to load tools into your agent. Once called, the output is ready for your vector store pipeline.

Absolutely, as long as you have indexed the output of `list_survey_responses`. You can then perform semantic searches across your feedback.

It is designed to work with the standard tool specs. You just pass the tools to your FunctionAgent and start querying.

The tool outputs become part of your local index. You maintain control over how long that data resides in your storage.

We enforce strict access controls on every `get_contact_profile` call. Your sensitive survey metadata stays private within your configured environment.

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