kvCORE MCP Server for LlamaIndex 10 tools — connect in under 2 minutes
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add kvCORE as an MCP tool provider through Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.
ASK AI ABOUT THIS MCP SERVER
Vinkius supports streamable HTTP and SSE.
import asyncio
from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI
async def main():
# Your Vinkius token. get it at cloud.vinkius.com
mcp_client = BasicMCPClient("https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")
mcp_tool_spec = McpToolSpec(client=mcp_client)
tools = await mcp_tool_spec.to_tool_list_async()
agent = FunctionAgent(
tools=tools,
llm=OpenAI(model="gpt-4o"),
system_prompt=(
"You are an assistant with access to kvCORE. "
"You have 10 tools available."
),
)
response = await agent.run(
"What tools are available in kvCORE?"
)
print(response)
asyncio.run(main())
* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure
About kvCORE MCP Server
Connect your AI agent to kvCORE, the primary platform for real estate professionals to manage their entire business.
LlamaIndex agents combine kvCORE tool responses with indexed documents for comprehensive, grounded answers. Connect 10 tools through Vinkius and query live data alongside vector stores and SQL databases in a single turn. ideal for hybrid search, data enrichment, and analytical workflows.
Key Features
- Lead Management — Search and audit lead profiles, update statuses, and add manual notes through natural language
- Listing Intelligence — Access active property listings and detailed metadata including features and pricing
- Marketing Visibility — List and monitor smart campaigns and automated marketing sequences
- Agent Workflow — Audit pending tasks, reminders, and recent lead activities to stay on top of follow-ups
- Profile Insights — Fetch agent profile data and high-level account configuration
Simple Setup
1. Subscribe to this server
2. Log in to kvCORE, go to Settings > API, and generate an API Key
3. Enter your key in the configuration panel
4. Start managing your real estate business via chat
The kvCORE MCP Server exposes 10 tools through the Vinkius. Connect it to LlamaIndex in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.
How to Connect kvCORE to LlamaIndex via MCP
Follow these steps to integrate the kvCORE MCP Server with LlamaIndex.
Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai
Replace the token
Replace [YOUR_TOKEN_HERE] with your Vinkius token
Run the agent
Save to agent.py and run: python agent.py
Explore tools
The agent discovers 10 tools from kvCORE
Why Use LlamaIndex with the kvCORE MCP Server
LlamaIndex provides unique advantages when paired with kvCORE through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine kvCORE tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain kvCORE tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query kvCORE, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what kvCORE tools were called, what data was returned, and how it influenced the final answer
kvCORE + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the kvCORE MCP Server delivers measurable value.
Hybrid search: combine kvCORE real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query kvCORE to augment indexed data with live information before generating user-facing responses
Knowledge base agents: build agents that maintain and update knowledge bases by periodically querying kvCORE for fresh data
Analytical workflows: chain kvCORE queries with LlamaIndex's data connectors to build multi-source analytical reports
kvCORE MCP Tools for LlamaIndex (10)
These 10 tools become available when you connect kvCORE to LlamaIndex via MCP:
create_lead_note
Add a note to a lead profile
get_agent_profile
Get current agent information
get_lead_details
Get details for a specific lead
get_listing_details
Get metadata for a specific listing
list_agent_tasks
List pending tasks for the agent
list_lead_activity
List recent activity for a lead
list_marketing_campaigns
List all marketing campaigns
list_property_listings
List active property listings
search_kvcore_leads
Returns lead IDs and basic contact info. Search for leads in kvCORE
update_lead_info
g., status, phone). Provide data as a JSON string. Update an existing lead
Example Prompts for kvCORE in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with kvCORE immediately.
"Search for a lead named 'Alice' in my kvCORE"
"Show me details for the property at '123 Maple St'"
"List my tasks for today"
Troubleshooting kvCORE MCP Server with LlamaIndex
Common issues when connecting kvCORE to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpkvCORE + LlamaIndex FAQ
Common questions about integrating kvCORE MCP Server with LlamaIndex.
How does LlamaIndex connect to MCP servers?
Can I combine MCP tools with vector stores?
Does LlamaIndex support async MCP calls?
Connect kvCORE with your favorite client
Step-by-step setup guides for every MCP-compatible client and framework:
Anthropic's native desktop app for Claude with built-in MCP support.
AI-first code editor with integrated LLM-powered coding assistance.
GitHub Copilot in VS Code with Agent mode and MCP support.
Purpose-built IDE for agentic AI coding workflows.
Autonomous AI coding agent that runs inside VS Code.
Anthropic's agentic CLI for terminal-first development.
Python SDK for building production-grade OpenAI agent workflows.
Google's framework for building production AI agents.
Type-safe agent development for Python with first-class MCP support.
TypeScript toolkit for building AI-powered web applications.
TypeScript-native agent framework for modern web stacks.
Python framework for orchestrating collaborative AI agent crews.
Leading Python framework for composable LLM applications.
Data-aware AI agent framework for structured and unstructured sources.
Microsoft's framework for multi-agent collaborative conversations.
Connect kvCORE to LlamaIndex
Get your token, paste the configuration, and start using 10 tools in under 2 minutes. No API key management needed.