How to Use the LandTech MCP in LlamaIndex

Q: How do I load LandTech tools into my LlamaIndex agent?

Install llama-index-tools-mcp and configure a McpToolSpec with your client. Pass the resulting tool list to your FunctionAgent.

Q: Can LlamaIndex index LandTech planning applications?

It indexes the exact outputs. You run searchurbanplanning, and LlamaIndex stores the returned application history in your vector database for future semantic queries.

Q: Does the LandTech MCP Server support data resource access?

You can enable includeresources=True during setup. This allows your agent to read raw site constraint files directly into the document store.

Q: How do I check ownership titles with LandTech and LlamaIndex?

The agent calls getownershiptitle when a user asks about a specific parcel. It retrieves the registered owner and immediately adds that fact to the active query context.

Index property titles and zoning laws into your LlamaIndex applications to query land viability.

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Connect LandTech MCP to LlamaIndex

Create your Vinkius account to connect LandTech 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 LandTech with LlamaIndex

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Perplexity

Ingest planning rules into vector stores

The `get_planning_policy` tool pulls dense local authority guidelines straight into your LlamaIndex knowledge base. Your application indexes the text, turning thousands of pages of municipal zoning laws into searchable embeddings. You query your agent about specific density limits or height restrictions, and it answers using grounded data. Instead of guessing building codes, the system retrieves exact clauses from the indexed `list_local_authority_plans` output.

Build searchable property portfolios

The `get_building_data` tool extracts square footage, build dates, and structural specs for your target assets. You route this data alongside `list_saved_sites` into a unified index of your development pipeline. Your acquisition team runs semantic searches across the portfolio. They ask the agent to find all sites with specific characteristics, and it cross-references the indexed building facts with `get_price_comparables` to rank the most profitable opportunities.

Ground site constraints in real data using this MCP Server

The `get_site_constraints` tool identifies physical and legal barriers like flood plains or protected habitats. LlamaIndex captures these risk factors and embeds them alongside your internal memos. When evaluating a new plot via `search_land_parcels`, the agent compares the current coordinates against historical constraint data. You get a factual risk assessment based entirely on the retrieved geospatial intelligence. If it hits a flood zone, you pass.

Setup guide

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

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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 LlamaIndex

Install `llama-index-tools-mcp` and configure a `McpToolSpec` with your client. Pass the resulting tool list to your `FunctionAgent`.

It indexes the exact outputs. You run `search_urban_planning`, and LlamaIndex stores the returned application history in your vector database for future semantic queries.

You can enable `include_resources=True` during setup. This allows your agent to read raw site constraint files directly into the document store.

The agent calls `get_ownership_title` when a user asks about a specific parcel. It retrieves the registered owner and immediately adds that fact to the active query context.

The server transmits regional sales data and freehold ownership records. Vinkius isolates every request in an ephemeral sandbox, ensuring your proprietary RAG queries never leak into shared memory.

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