How to Use the kvCORE MCP in LlamaIndex

Q: How do I initialize kvCORE MCP tools in LlamaIndex?

Install llama-index-tools-mcp and set up a BasicMCPClient. Call await mcptoolspec.totoollistasync() to get the functions ready.

Q: Can LlamaIndex search through my kvCORE leads?

It can. You run searchkvcoreleads and index the output. Then you just query your vector store for specific client profiles.

Q: Does the server allow LlamaIndex to modify records?

Yes. You can include createleadnote in your allowed tools filter. Your agent will push updates back to the CRM when needed.

Index your real estate data into searchable LlamaIndex knowledge bases.

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Connect kvCORE MCP to LlamaIndex

Create your Vinkius account to connect kvCORE 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 kvCORE with LlamaIndex

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Build RAG with kvCORE Leads

The `get_lead_details` tool pulls specific contact records straight into your LlamaIndex workflow. Instead of just reading the data, your application indexes it into a vector store for semantic search later. You combine this with `list_lead_activity` to capture a full history of client interactions. When you query your RAG application, you get answers grounded in actual CRM data rather than AI hallucinations.

Query the kvCORE MCP Server

Run `list_property_listings` to fetch active properties and feed them into your knowledge base. Your agent uses `get_listing_details` to grab the metadata for each one. Now you have a unified, queryable index of your entire real estate portfolio. You ask natural language questions about active listings, and the system pulls exact matches from the indexed tool outputs.

Index Marketing Operations

Call `list_marketing_campaigns` to extract active marketing efforts alongside `get_agent_profile`. You pass these through `McpToolSpec` straight into your FunctionAgent. The agent cross-references campaign data with `list_agent_tasks`. You build an application that knows exactly what your agents are supposed to be doing and which campaigns they are running.

Setup guide

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

Install `llama-index-tools-mcp` and set up a `BasicMCPClient`. Call `await mcp_tool_spec.to_tool_list_async()` to get the functions ready.

It can. You run `search_kvcore_leads` and index the output. Then you just query your vector store for specific client profiles.

Yes. You can include `create_lead_note` in your allowed tools filter. Your agent will push updates back to the CRM when needed.

That's the entire point of RAG. You index your property PDFs alongside live API data from `get_listing_details` to create a single queryable source.

The connection handles sensitive property metadata and agent IDs. Every MCP tool call operates through a zero-trust architecture, ensuring no persistent storage of your LlamaIndex vector data on the host machine.

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