How to Use the NCREIF Custom Query MCP in CrewAI

Q: How do I assign NCREIF Custom Query to a CrewAI agent?

Pass the server URL into your agent's mcps list. The agent will automatically recognize the tools and integrate them into its planning process.

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Connect NCREIF Custom Query MCP to CrewAI

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

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Perplexity

Autonomous index research teams

Assign a dedicated agent to research NPI trends using `get_historical_npi`. The agent can pull data as needed while the rest of your crew processes the results. This specialization allows your agents to focus on deep analysis rather than manual data gathering. It keeps your operations moving at scale.

Targeted SQL query agents

Give your data-focused agent access to `execute_query` to extract precise snapshots from the database. This agent works alongside others to validate findings. By isolating the query tool to one agent, you maintain strict control over how your system interacts with the source. It prevents redundant or conflicting requests.

KPI monitoring agents

Use `get_predefined_kpi` to have an agent monitor performance metrics continuously. It can flag deviations and report them to the moderator agent. This keeps your team informed without needing manual check-ins. It automates the routine monitoring of institutional performance benchmarks.

Setup guide

Set up NCREIF Custom Query MCP in CrewAI

Prerequisites

Python 3.10+ installed
crewai package (pip install crewai)
Active Vinkius subscription with a valid endpoint token

1

Install CrewAI
Run pip install crewai to install the framework. MCP support is built-in via the mcps parameter.
2

Add the MCP URL to your agent
Pass your Vinkius endpoint directly to the mcps list. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. CrewAI handles tool discovery and caching automatically.
3

Kick off your crew
Create a Crew with your agent and tasks. Call crew.kickoff() — the agent will automatically invoke NCREIF Custom Query tools as needed.

crew.py

from crewai import Agent, Task, Crew

agent = Agent(
    role="NCREIF Custom Query Analyst",
    goal="Access and analyze NCREIF Custom Query data via MCP.",
    backstory="Expert analyst with direct NCREIF Custom Query access.",
    mcps=[
        "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
    ],
)

task = Task(
    description="List recent NCREIF Custom Query transactions",
    agent=agent,
    expected_output="A summary of recent activity",
)

crew = Crew(agents=[agent], tasks=[task])
result = crew.kickoff()
print(result)

Get your connection token →

Prerequisites

Python 3.10+ installed
crewai + crewai-tools packages
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install crewai crewai-tools. The MCPServerAdapter handles lifecycle management and tool conversion.
2

Connect with MCPServerAdapter
Use MCPServerAdapter as a context manager with SseServerParameters pointing to your Vinkius endpoint. The adapter automatically manages connection lifecycle.
3

Assign tools and run
Pass the returned mcp_tools to your agent's tools parameter. The adapter converts MCP tools to native BaseTool objects compatible with all CrewAI agents.

crew.py

from crewai import Agent, Task, Crew
from crewai_tools import MCPServerAdapter
from mcp import SseServerParameters

server_params = SseServerParameters(
    url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)

with MCPServerAdapter(server_params) as mcp_tools:
    agent = Agent(
        role="NCREIF Custom Query Analyst",
        goal="Access and analyze NCREIF Custom Query data via MCP.",
        backstory="Expert analyst with direct NCREIF Custom Query access.",
        tools=mcp_tools,
    )

    task = Task(
        description="List recent NCREIF Custom Query transactions",
        agent=agent,
        expected_output="A summary of recent activity",
    )

    crew = Crew(agents=[agent], tasks=[task])
    result = crew.kickoff()
    print(result)

Get your connection token →

Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by NCREIF. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.

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Common questions about NCREIF Custom Query MCP in CrewAI

Pass the server URL into your agent's `mcps` list. The agent will automatically recognize the tools and integrate them into its planning process.

You can. Use the tool filter in your MCP configuration to ensure only the specific agent needing the data can access the NCREIF Custom Query tools.

CrewAI agents can interpret tool errors and adjust their strategy. If a query returns no data, the agent can try a different parameter or report the issue to the crew lead.

The data is handled entirely within the secure, isolated environment of the MCP Server. No index information is shared between agents unless the crew structure explicitly allows it.

Yes. You can place the server at the manager level or assign it to a sub-team. It scales with your crew's complexity.

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