How to Use the NCREIF Custom Query MCP in Mastra AI

Q: Is NCREIF Custom Query compatible with Mastra AI tool approval?

It is. You can set requireToolApproval in your agent configuration. This forces a human to review the query before it runs against the NCREIF database.

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Connect NCREIF Custom Query MCP to Mastra AI

Create your Vinkius account to connect NCREIF Custom Query to Mastra AI 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 Mastra AI

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Perplexity

Automated index workflow branching

Use `get_historical_npi` within your Mastra workflows to drive conditional logic. If the index return hits a specific threshold, your agent can trigger an automated alert or rebalancing step. This setup handles failures and retries automatically. It ensures your data pipeline keeps running even if the network blips.

Custom SQL for complex data tasks

Trigger `execute_query` to pull specific sector or regional data based on your workflow requirements. Mastra treats these queries as first-class steps in your automation. This lets you build complex logic that depends on raw database hits. It keeps your business rules clean and separated from the data fetching layer.

KPI validation in agents

Call `get_predefined_kpi` to verify performance metrics before your agents proceed with a task. This acts as a gatekeeper for your multi-step processes. If the data doesn't meet your criteria, the workflow stops or routes to a human operator. It prevents bad data from propagating through your system.

Setup guide

Set up NCREIF Custom Query MCP in Mastra AI

Prerequisites

Node.js 18+ and a TypeScript project
@mastra/mcp + @mastra/core packages
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run npm install @mastra/mcp @mastra/core plus your preferred model provider (e.g. @ai-sdk/openai).
2

Configure the MCPClient
Create an MCPClient with your Vinkius endpoint as a URL object. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Discover and inject tools
Call mcpClient.listTools() and spread the result into your agent's tools object. All NCREIF Custom Query tools become native Mastra tools.
4

Run with any model
Swap openai("gpt-4o") for any AI SDK-compatible provider. Call agent.generate() and the agent routes tool calls through MCP automatically.

agent.ts

import { MCPClient } from "@mastra/mcp";
import { Agent } from "@mastra/core/agent";
import { openai } from "@ai-sdk/openai";

const mcpClient = new MCPClient({
  id: "ncreif-custom-query-mcp-client",
  servers: {
    "ncreif-custom-query-mcp": {
      url: new URL(
        "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
      ),
    },
  },
});

const agent = new Agent({
  name: "NCREIF Custom Query Agent",
  model: openai("gpt-4o"),
  instructions: "You have access to NCREIF Custom Query tools.",
  tools: {
    ...(await mcpClient.listTools()),
  },
});

const result = await agent.generate(
  "List recent NCREIF Custom Query transactions"
);
console.log(result.text);

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Common questions about NCREIF Custom Query MCP in Mastra AI

You register the server in your Mastra client configuration. Once added, your agents see the tools and can invoke them as part of any defined workflow.

Yes. Mastra supports exponential backoff for tool calls. If a query fails, the agent will automatically try again based on your defined policy.

It is. You can set `requireToolApproval` in your agent configuration. This forces a human to review the query before it runs against the NCREIF database.

The server operates in an ephemeral sandbox. It only processes the index records requested by your agent and clears the memory immediately after the task completes.

It works natively. You can chain these tools with other actions to create full-scale automation that doesn't require constant monitoring.

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