How to Use the LandTech MCP in CrewAI

Q: How do I assign LandTech MCP Server tools to specific CrewAI agents?

Pass your Vinkius server URL into the mcps parameter when defining your CrewAI agents. You can restrict tools so only your researcher agent can call searchlandparcels, while your legal agent gets access to getownershiptitle.

Q: How do CrewAI agents handle complex LandTech planning policy lookups?

Your agents can use searchurbanplanning to find relevant applications, then pass the results to a policy specialist agent. This specialist uses getplanningpolicy to interpret local development rules and make structured recommendations.

Q: Is our proprietary portfolio list safe from exposure during CrewAI runs?

Yes, all portfolio data pulled via listsavedsites is processed locally within your CrewAI environment and Vinkius's zero-trust MCP Server sandbox. Your proprietary list of target development sites is never exposed to third-party model providers.

Deploy a collaborative crew of specialized agents to research land ownership and analyze site constraints using CrewAI.

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Connect LandTech MCP to CrewAI

Create your Vinkius account to connect LandTech 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 LandTech with CrewAI

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Perplexity

Run multi-agent land acquisition teams in CrewAI

The `search_land_parcels` tool allows your CrewAI researcher agent to scan specific coordinate ranges for viable development sites. Once a list of parcels is compiled, the researcher passes the IDs to an analyst agent who owns the next step. This collaborative structure means your agents share memory and context. The analyst doesn't have to re-query the coordinates; it simply takes the parcel IDs and moves to the next phase of the pipeline.

Divide and conquer zoning checks with an MCP Server

The `get_planning_details` tool extracts active planning histories, which your regulatory agent can cross-reference with zoning guidelines. While that agent analyzes policies, a separate finance agent runs `get_price_comparables` to evaluate local market trends. CrewAI coordinates these tasks sequentially or hierarchically. The math doesn't lie — the final output is a combined feasibility report that merges planning restrictions with market pricing without manual intervention.

Autonomous portfolio monitoring for investment crews

The `list_saved_sites` tool retrieves your current portfolio so your monitoring crew can audit them for changes. A specialized auditor agent runs daily checks on these saved sites using `get_real_estate_market_data` to track regional growth trends. If market indicators shift, the supervisor agent tasks a writer agent with drafting an investment memo. The entire process runs autonomously in the background of your server environment.

Setup guide

Set up LandTech 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 LandTech tools as needed.

crew.py

from crewai import Agent, Task, Crew

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

task = Task(
    description="List recent LandTech 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="LandTech Analyst",
        goal="Access and analyze LandTech data via MCP.",
        backstory="Expert analyst with direct LandTech access.",
        tools=mcp_tools,
    )

    task = Task(
        description="List recent LandTech 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 LandTech. 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 LandTech MCP in CrewAI

Pass your Vinkius server URL into the `mcps` parameter when defining your CrewAI agents. You can restrict tools so only your researcher agent can call `search_land_parcels`, while your legal agent gets access to `get_ownership_title`.

Yes, you can task an agent with auditing site viability by giving it access to `get_site_constraints`. The agent will call this tool to check for environmental protections and compile its findings into your shared crew memory.

Your agents can use `search_urban_planning` to find relevant applications, then pass the results to a policy specialist agent. This specialist uses `get_planning_policy` to interpret local development rules and make structured recommendations.

CrewAI supports stdio, SSE, and Streamable HTTP transports. You can configure the `MCPServerHTTP` class to filter specific tools, ensuring your agents only access the exact endpoints needed for their roles.

Yes, all portfolio data pulled via `list_saved_sites` is processed locally within your CrewAI environment and Vinkius's zero-trust MCP Server sandbox. Your proprietary list of target development sites is never exposed to third-party model providers.

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