Model Context Protocol (MCP)
The property-data backbone powering PriceHubble AI Agents
PriceHubble AI Agents rely on a simple principle: LLMs are only as good as the data you give them.
To guarantee accuracy, safety, and enterprise-grade reliability, we have built and deployed our own Model Context Protocol (MCP) server. It allows AI agents to retrieve property insights directly from our infrastructure. MCPs are the missing infrastructure layer that connects autonomous agents with verified property data, in real time and at scale.
The property-data backbone powering PriceHubble AI Agents
PriceHubble AI Agents rely on a simple principle: LLMs are only as good as the data you give them.
To guarantee accuracy, safety, and enterprise-grade reliability, we have built and deployed our own Model Context Protocol (MCP) server. It allows AI agents to retrieve property insights directly from our infrastructure. MCPs are the missing infrastructure layer that connects autonomous agents with verified property data, in real time and at scale.
What is an MCP?
An MCP is a structured, agent-oriented interface that allows LLM-powered systems to request contextual data safely.
Where an API enables communication between applications, an MCP is built specifically for agentic workflows, providing structured context, granular permissions, deterministic responses, and unified access to curated datasets
APIs were made for applications. MCPs are made for AI Agents.
Launching a Model Context Protocol suite to power more reliable AI property insights
PriceHubble launches an MCP offering dedicated to property data, covering 10 countries and powered by our proprietary data lake and models. Each MCP endpoint exposes a specific domain of the PriceHubble intelligence stack, such as:
Launching a Model Context Protocol suite to power more reliable AI property insights
PriceHubble launches an MCP offering dedicated to property data, covering 10 countries and powered by our proprietary data lake and models. Each MCP endpoint exposes a specific domain of the PriceHubble intelligence stack, such as:
More MCP endpoints are released by the day as we continue expanding our agentic capabilities.This enables agents to act on real, validated, and localised property insights instead of relying on assumptions or approximations.
More MCP endpoints are released by the day as we continue expanding our agentic capabilities This enables agents to act on real, validated, and localised property insights instead of relying on assumptions or approximations.
How MCPs power our AI Agents
The Companion Agent uses MCP endpoints to deliver personalised insights to property owners on their property (valuation, local trends, relevant listings).
The Copilot Agent (personal assistant) relies on MCP endpoints to navigate transactions, filter comparables, generate market analyses, and surface actionable insights.
This architecture ensures that all agent outputs are grounded in PriceHubble data, consistent across markets, deterministic and fully auditable and ready for large-scale enterprise deployments.
How MCPs power our AI Agents
The Companion Agent uses MCP endpoints to deliver personalised insights to property owners on their property (valuation, local trends, relevant listings).
The Copilot Agent (personal assistant) relies on MCP endpoints to navigate transactions, filter comparables, generate market analyses, and surface actionable insights.
This architecture ensures that all agent outputs are grounded in PriceHubble data, consistent across markets, deterministic and fully auditable and ready for large-scale enterprise deployments.
Why MCPs matter for AI Agents
MCPs give agents a dependable foundation to operate with accuracy, consistency, and trust from the very first interaction.
How MCPs power our AI Agents
The Companion Agent uses MCP endpoints to deliver personalised insights to property owners on their property (valuation, local trends, relevant listings).
The Copilot Agent (personal assistant) relies on MCP endpoints to navigate transactions, filter comparables, generate market analyses, and surface actionable insights.
This architecture ensures that all agent outputs are grounded in PriceHubble data, consistent across markets, deterministic and fully auditable and ready for large-scale enterprise deployments.
From Q2 2026, our MCPs will be available to external customers through an early access beta, allowing you to plug directly into the same property data infrastructure we use internally. If you’d like to explore how MCP can support your AI strategy or begin integrating it into your agentic workflows, please reach out to our team.
Become an early adopter
Become an early adopter
Let’s build the next generation of property-intelligence agents together.
Request a demo
Request a demo
We will get back to you quickly.
We look forward to speaking with you.
Request a demo
We will get back to you quickly.
We look forward to speaking with you.
Thank you!
We will get back to you within 24 business hours.
Thank you!
We will get back to you within 24 business hours.
Dataloft has now merged with PriceHubble.com