NCREIF 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 NCREIF as an MCP tool provider through the 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 NCREIF. "
"You have 10 tools available."
),
)
response = await agent.run(
"What tools are available in NCREIF?"
)
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 NCREIF MCP Server
Connect your NCREIF account to your AI agent and gain authoritative insights into the institutional commercial real estate market through natural conversation.
LlamaIndex agents combine NCREIF tool responses with indexed documents for comprehensive, grounded answers. Connect 10 tools through the Vinkius and query live data alongside vector stores and SQL databases in a single turn — ideal for hybrid search, data enrichment, and analytical workflows.
What you can do
- Index Performance — List and retrieve historical data for NCREIF indices such as the NPI (Property Index) and ODCE (Fund Index).
- Property Oversight — List indexed properties and fetch detailed historical returns and performance metrics.
- Market Analysis — Access high-level real estate market data and aggregated performance by region or property type (Office, Retail, etc.).
- Fund Tracking — View all tracked real estate investment funds and their performance history.
- Data Series Access — Browse granular data series and categories for in-depth real estate research.
- Deep Inspection — Fetch complete metadata for specific indices, properties, or funds using their unique IDs.
The NCREIF 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 NCREIF to LlamaIndex via MCP
Follow these steps to integrate the NCREIF 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 NCREIF
Why Use LlamaIndex with the NCREIF MCP Server
LlamaIndex provides unique advantages when paired with NCREIF through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine NCREIF tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain NCREIF tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query NCREIF, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what NCREIF tools were called, what data was returned, and how it influenced the final answer
NCREIF + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the NCREIF MCP Server delivers measurable value.
Hybrid search: combine NCREIF real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query NCREIF 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 NCREIF for fresh data
Analytical workflows: chain NCREIF queries with LlamaIndex's data connectors to build multi-source analytical reports
NCREIF MCP Tools for LlamaIndex (10)
These 10 tools become available when you connect NCREIF to LlamaIndex via MCP:
get_fund_performance
Get specific fund performance
get_index_data
Get specific index data
get_property_returns
Get property-level returns
get_property_type_data
g., Office, Retail, Industrial). Get data by property type
get_region_data
Get performance data by region
list_data_series
List available data series
list_funds
List real estate funds
list_indices
g., NPI, ODCE). List NCREIF performance indices
list_market_data
List real estate market data
list_properties
List indexed properties
Example Prompts for NCREIF in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with NCREIF immediately.
"List all commercial real estate indices available via NCREIF."
"Show me the performance data for the 'Office' property type."
"What is the recent performance history for the ODCE Fund Index?"
Troubleshooting NCREIF MCP Server with LlamaIndex
Common issues when connecting NCREIF to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpNCREIF + LlamaIndex FAQ
Common questions about integrating NCREIF 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 NCREIF 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 NCREIF to LlamaIndex
Get your token, paste the configuration, and start using 10 tools in under 2 minutes. No API key management needed.