How to Use the Green Street MCP in LlamaIndex

Q: How do I load the Green Street MCP server into my LlamaIndex pipeline?

Install the MCP tool package using pip install llama-index-tools-mcp first. Next, instantiate the BasicMCPClient with your Vinkius URL and convert it to a tool list using McpToolSpec.

Q: Can LlamaIndex store the output of Green Street tools for offline search?

Yes, you can parse the JSON responses from tools like getforecastscenarios and insert them directly into a VectorStoreIndex. This lets you run semantic search queries over the real estate data without hitting the API repeatedly.

Q: Does LlamaIndex support filtering specific Green Street tools?

You can use the allowedtools filter when initializing your tool specification to expose only what you need. This keeps your agent focused on specific tasks, like retrieving transaction details instead of pulling news.

Index live commercial real estate valuations directly into your LlamaIndex vector stores for accurate, grounded RAG applications.

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Connect Green Street MCP to LlamaIndex

Create your Vinkius account to connect Green Street 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 Green Street with LlamaIndex

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Build Grounded Real Estate RAG with LlamaIndex

This MCP Server uses `get_news_articles` to fetch current commercial real estate articles and feed them directly into your index. Instead of letting your model guess, you can index these live articles and search them semantically alongside your internal documents. Grounding your queries in real data prevents the model from hallucinating numbers. When a user asks about sector trends, LlamaIndex pulls the latest news and combines it with your local vector store to build a highly accurate response.

Index Live REIT Metrics into Vector Stores

The `get_nav_estimates` tool provides real-time Net Asset Value calculations that your LlamaIndex MCP configuration can index on the fly. By turning these live API responses into document nodes, you make financial data searchable via natural language. You can combine this with `get_earnings_metrics` to build a dynamic knowledge base. Your agent queries the API, converts the JSON payload into an indexable format, and updates your vector store so your queries always use the latest FFO data.

Query Market Grades and Sector Analytics

The `get_market_sector_summary` tool retrieves detailed analytics for specific property types to help you evaluate regional performance. Your agent can pull these summaries and store them in an active index to answer complex questions about market health. Running `list_companies` gives your agent a clean directory of active REITs to map against these market grades. This lets you build a queryable database where local market health and corporate profiles are linked semantically.

Setup guide

Set up Green Street 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 Green Street 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 Green Street tools.",
)
response = await agent.run("List recent Green Street 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 Green Street. 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 Green Street MCP in LlamaIndex

Install the MCP tool package using `pip install llama-index-tools-mcp` first. Next, instantiate the `BasicMCPClient` with your Vinkius URL and convert it to a tool list using `McpToolSpec`.

Yes, you can parse the JSON responses from tools like `get_forecast_scenarios` and insert them directly into a VectorStoreIndex. This lets you run semantic search queries over the real estate data without hitting the API repeatedly.

You can use the `allowed_tools` filter when initializing your tool specification to expose only what you need. This keeps your agent focused on specific tasks, like retrieving transaction details instead of pulling news.

Vinkius manages the authentication layer, meaning you only need to supply your single Vinkius endpoint token. You don't have to write custom authentication handlers or store multiple API keys in your environment.

Every MCP request is processed through an ephemeral, zero-trust sandbox that destroys itself immediately after execution. Your proprietary financial queries and market analyses are never cached or used for training.

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