How to Use the NCREIF Custom Query MCP in LlamaIndex

Q: Can I filter NCREIF Custom Query tools in my LlamaIndex agent?

Yes, you can use the allowedtools filter to restrict which functions your agent can access. This gives you granular control over what data your RAG application can pull.

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Connect NCREIF Custom Query MCP to LlamaIndex

Create your Vinkius account to connect NCREIF Custom Query 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 NCREIF Custom Query with LlamaIndex

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Index historical NPI data for semantic search

Feed the output of `get_historical_npi` into your LlamaIndex vector store. This allows your RAG application to query past index performance using natural language. Your agents can now compare current market conditions against historical benchmarks stored in your local index. It turns raw NCREIF data into a searchable institutional memory.

Execute custom SQL queries for RAG context

The `execute_query` tool lets you fetch specific subsets of real estate data to ground your LLM's responses. You can pull targeted sector results and index them for future retrieval. This makes your knowledge base highly specific to your portfolio's needs. You aren't just searching documents; you're querying the live index database.

Integrate predefined KPIs into your knowledge graph

Use `get_predefined_kpi` to populate your index with verified performance figures. These metrics serve as authoritative facts that your agent can cite in reports. By unifying these KPIs with your existing documents, you create a source of truth for your analysts. It ensures that every AI-generated insight is backed by reliable NCREIF data.

Setup guide

Set up NCREIF Custom Query 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 NCREIF Custom Query 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 NCREIF Custom Query tools.",
)
response = await agent.run("List recent NCREIF Custom Query data")

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Common questions about NCREIF Custom Query MCP in LlamaIndex

You use the provided tool spec to execute queries and then ingest the structured JSON response into your vector store. It creates a bridge between real-time API data and your semantic search layer.

By grounding your agent in retrieved NPI data, you force the model to stick to the actual index values. It pulls the specific numbers from the source, minimizing the risk of fabricated performance data.

Yes, you can use the allowed_tools filter to restrict which functions your agent can access. This gives you granular control over what data your RAG application can pull.

The McpToolAdapter automatically converts the server schema into a format LlamaIndex understands. You get full access to all three tools without manual schema mapping.

Your connection is secured via an ephemeral V8 sandbox. Only the raw data you explicitly fetch enters your index, and all communication is encrypted.

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