How to Use the Kustomer MCP in LlamaIndex

Q: Can LlamaIndex query Kustomer custom data classes?

Yes. Your LlamaIndex agent calls listdataklasses to understand the custom metadata structure, allowing the RAG pipeline to filter search results based on your specific setup.

Q: How does LlamaIndex search the Kustomer timeline?

The agent uses searchkustomertimeline with structured JSON filters. This retrieves historical events, which LlamaIndex then parses to answer complex questions about the customer's history.

Q: Can I check the Kustomer API status before indexing with LlamaIndex?

Yes. You can have your LlamaIndex script call checkkustomerapistatus at the start of your indexing job to ensure the endpoint is online before making heavy pull requests.

Index live Kustomer support history into LlamaIndex to run RAG on actual customer conversations without hallucinations.

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Connect Kustomer MCP to LlamaIndex

Create your Vinkius account to connect Kustomer 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 Kustomer with LlamaIndex

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Index Kustomer timelines for LlamaIndex RAG

This MCP Server exposes `search_kustomer_timeline` to pull live support history directly into your LlamaIndex vector store. Your LlamaIndex agent queries this index to find patterns in past issues. By calling `get_conversation_details`, the framework injects raw, structured support data directly into the LLM context window.

Ground LlamaIndex queries in customer profiles

This MCP Server provides `get_customer_profile` to retrieve verified metadata before your LlamaIndex agent answers user queries. This integration ensures that custom customer attributes are respected. Your LlamaIndex agent reads custom schemas via the MCP server using `list_data_klasses` to map out the exact data structures.

Audit conversations using LlamaIndex tools

This MCP Server lets your LlamaIndex pipeline call `list_support_conversations` and `list_conversation_messages` to fetch recent support transcripts. This lets you run automated quality checks on your team's performance. By pulling active support agents via `list_kustomer_agents`, LlamaIndex maps specific conversation transcripts back to individual team members.

Setup guide

Set up Kustomer 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 Kustomer 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 Kustomer tools.",
)
response = await agent.run("List recent Kustomer 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 Kustomer. 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 Kustomer MCP in LlamaIndex

Install llama-index-tools-mcp and initialize the MCP client with your Vinkius URL. Convert the MCP Server tools into a list using to_tool_list_async() and pass them to your FunctionAgent.

Yes. Your LlamaIndex agent calls `list_data_klasses` to understand the custom metadata structure, allowing the RAG pipeline to filter search results based on your specific setup.

The agent uses `search_kustomer_timeline` with structured JSON filters. This retrieves historical events, which LlamaIndex then parses to answer complex questions about the customer's history.

Yes. You can have your LlamaIndex script call `check_kustomer_api_status` at the start of your indexing job to ensure the endpoint is online before making heavy pull requests.

Absolutely. This MCP Server handles sensitive customer identifiers and conversation logs via secure, short-lived sandboxes. No customer profiles retrieved by `list_kustomer_customers` are cached or written to persistent storage, satisfying strict corporate compliance requirements.

Use it with your favorite AI tools

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