How to Use the Bridge Data Output MCP in LlamaIndex

Q: Can LlamaIndex query Bridge Data Output to build local property indices?

Yes, your pipeline can call listproperties to fetch listings and index them into a vector store. This lets you run semantic searches over real-time real estate data.

Q: How does LlamaIndex handle schema changes from Bridge Data Output?

The server provides getdatasetmetadata to check schema structures dynamically. LlamaIndex uses this metadata to map incoming property fields to your index schema without breaking your pipeline.

Index live MLS listings into LlamaIndex vector stores to build semantic real estate search engines.

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Connect Bridge Data Output MCP to LlamaIndex

Create your Vinkius account to connect Bridge Data Output 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 Bridge Data Output with LlamaIndex

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Index live property data into your LlamaIndex knowledge base.

This MCP Server connects your indexer to live real estate data via `get_property` and `list_properties`. It pulls standardized MLS listings and converts them into queryable nodes. LlamaIndex takes this raw property data and indexes it for semantic retrieval. Your users can run natural language queries over the retrieved listings, bypassing traditional SQL filters entirely.

Search properties by city and price for RAG pipelines.

The `search_properties_by_city` and `search_properties_by_price` tools serve as dynamic data retrievers for your RAG applications. Your agent calls these tools to fetch targeted property sets before generating answers. This setup keeps your generation grounded in actual market data instead of outdated training weights. The agent retrieves matching homes first, then writes a market analysis based on those specific listings.

Extract agent, office, and media metadata.

The `list_members`, `list_offices`, and `list_media` tools pull broker directories and media links into your index. This adds rich metadata to your real estate knowledge graphs. Your query engine can resolve complex questions about which agents handle specific listings. By indexing this metadata, LlamaIndex connects properties to their listing offices automatically.

Setup guide

Set up Bridge Data Output 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 Bridge Data Output 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 Bridge Data Output tools.",
)
response = await agent.run("List recent Bridge Data Output 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 Bridge Data Output. 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 Bridge Data Output MCP in LlamaIndex

Use the BasicMCPClient to connect to your Vinkius endpoint, then wrap it in McpToolSpec. Convert the spec to a tool list and pass it to your FunctionAgent to start querying.

Yes, your pipeline can call `list_properties` to fetch listings and index them into a vector store. This lets you run semantic searches over real-time real estate data.

The server provides `get_dataset_metadata` to check schema structures dynamically. LlamaIndex uses this metadata to map incoming property fields to your index schema without breaking your pipeline.

Yes, you can use the allowed tools filter during initialization to restrict agent access to specific MCP tools. This ensures your agent only calls specific tools like `search_properties_by_city` and ignores administrative tools.

Absolutely, Vinkius uses zero-trust, ephemeral V8 isolates to run the MCP Server. Your queries, property lookups, and agent metadata are never stored or used to train models.

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