How to Use the NCREIF Custom Query MCP in LangChain

Chain your valuation logic directly into LangChain agents with NCREIF Custom Query data.

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Connect NCREIF Custom Query MCP to LangChain

Create your Vinkius account to connect NCREIF Custom Query to LangChain 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 LangChain

Ask AI about this MCP

ChatGPT

Claude

Perplexity

Dynamic SQL execution for LangChain pipelines

Use `execute_query` to pull raw NPI data directly into your agent's reasoning loop. You can define custom logic that transforms raw index results into actionable inputs for downstream nodes. This keeps your agent's decision-making grounded in institutional benchmarks. By chaining these calls, you avoid the latency of manual data preparation and minimize the risk of stale figures entering your model.

Sequence historical NPI lookups

The `get_historical_npi` tool acts as a dedicated step within your LangGraph workflow. You can fetch specific time-series data and pass it immediately to a vector store or a secondary analysis tool. Your agents can now iterate on historical performance without human intervention. This setup ensures that every index update is processed according to your defined chain architecture.

Automated KPI retrieval for agent observability

Integrate `get_predefined_kpi` to pull validated metrics into your LangSmith traces. This provides a clear audit trail of exactly what index numbers your agent used to reach a specific conclusion. It removes ambiguity from your multi-step pipelines. When your agent reports a yield calculation, you have a direct link back to the specific NCREIF source data.

Setup guide

Set up NCREIF Custom Query MCP in LangChain

Prerequisites

Python 3.10+ installed
langchain-mcp-adapters + langgraph packages
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install langchain-mcp-adapters langgraph langchain-openai. The MCP adapters package converts MCP tools into native LangChain BaseTool objects.
2

Connect via HTTP transport
Use MultiServerMCPClient with "transport": "http" pointing to your Vinkius endpoint. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Create a ReAct agent
Pass the discovered tools to create_react_agent() from LangGraph. The agent automatically routes NCREIF Custom Query tool calls through the MCP protocol.
4

Run with any LLM
Swap ChatOpenAI for ChatAnthropic, ChatGoogleGenerativeAI, or any LangChain-compatible model. The MCP tools work identically across all providers.

agent.py

from langchain_mcp_adapters.client import MultiServerMCPClient
from langgraph.prebuilt import create_react_agent
from langchain_openai import ChatOpenAI

async with MultiServerMCPClient({
    "ncreif-custom-query-mcp": {
        "transport": "http",
        "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp",
    }
}) as client:
    tools = client.get_tools()

    agent = create_react_agent(
        ChatOpenAI(model="gpt-4o"),
        tools,
    )
    result = await agent.ainvoke({
        "messages": "List recent NCREIF Custom Query transactions"
    })
    print(result["messages"][-1].content)

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Common questions about NCREIF Custom Query MCP in LangChain

It provides a direct interface for your agents to call index data as standard tools. You define the sequence, and the server returns the specific NPI or KPI values required for the next link in your chain.

Yes, you pass the tool list directly to the agent constructor. The agent determines when to trigger a query based on the prompt requirements, keeping your logic responsive.

The server remains stateless by default, but you can manage persistent context using the LangChain client session. This allows you to maintain data continuity across complex, multi-step agent interactions.

The tool returns a clear error code that your chain can handle with standard logic. You can build retry loops or fallback paths to ensure your agent doesn't stall.

Vinkius handles authentication through a single endpoint token. The data stays within your authorized environment, and the connection is isolated at the infrastructure level.

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