Searches are fragmented
Listings, solds, rentals, market stats, and local caveats are rarely in one usable flow.
Search, compare, and reason over structured Ontario property data.
I would split this into ownership type, strongest sold support, neighbourhood trade-offs, and next actions. Under C$900K, condo townhomes dominate; freehold options need condition and location caveats.
Matches
12 likely options
Sold support
C$820K-C$895K
Next workflow
shortlist + alert
Want me to turn this into a client-ready brief and saved search?
ChatGPT can make mistakes. Check important info.
Platform proof
1.3M+
Ontario listing records organized for research
400+
Ontario municipalities represented
20+
registered property-intelligence tools
6+
research output families supported
ChatGPT + Codex
available AI-client surfaces
Reviewed access
MCP onboarding and data scope
What is agent0?
agent0 is an Ontario real-estate MCP server that connects ChatGPT, Codex, and approved AI workflows to structured property intelligence - including listings, sold comparables, rental context, market statistics, neighbourhood data, and research-ready outputs.
The gap
Professional property questions still require jumping between searches, spreadsheets, sold records, rental notes, neighbourhood context, and client-facing explanations. agent0 gives AI agents a structured way to retrieve and package that context.
Listings, solds, rentals, market stats, and local caveats are rarely in one usable flow.
Professionals need briefs, comps, explanations, and shortlists, not just another result list.
Property data needs access controls, caveats, source limits, and human review built into the answer.
What agent0 can answer
Compare cities, price bands, property types, active options, and likely trade-offs for buyer research.
Pull comparable-sale context with plain-language caveats a professional can review before use.
Summarize relevant rental comparables, local pressure, and assumptions for landlord or investor research.
Turn neighbourhood and municipality context into concise comparisons for location decisions.
Move from a broad question to candidate properties, saved criteria, follow-up filters, and alerts.
Produce buyer reports, market briefs, CMA support notes, and media-ready explainers for human editing.
Where it works
The homepage should reflect where agent0 works today and where it plans to work next, without implying public access or completed integrations that are still planned.
ChatGPT
AvailableStructured property answers and MCP tool calls inside approved ChatGPT use.
Codex
AvailableDeveloper and operator research context for AI-assisted build sessions.
Claude
PlannedAdditional assistant surface planned for reviewed property-intelligence use.
Claude Code
PlannedPlanned developer support for structured Ontario property context.
MCP access
ReviewedRemote server access, approved tools, authentication, and reviewed data scope.
How it works
The visitor path is simple: agent0 receives a professional property question, retrieves structured context, normalizes the answer, and returns an output that can be reviewed.
01 - Ask
Use ChatGPT, Codex, or an approved MCP client to ask for comps, rental context, market movement, or a buyer brief.
02 - Retrieve
agent0 routes the request through structured tools for listings, sold context, stats, areas, buildings, shortlists, and alerts.
03 - Normalize
Results are shaped around property type, geography, date range, access level, source limitations, and review caveats.
04 - Produce
The answer becomes a market brief, comparable summary, buyer report, shortlist, alert, or research note.
Product example
Detailed pages can carry implementation depth. The homepage should show the kind of output agent0 produces, with the right caveats and without pretending these are live statistics.
Example prompt
Compare $800K first-time buyer options across Ajax, Whitby, Oshawa, Scarborough, and Markham.
Illustrative only. This example is not live market data, pricing advice, legal advice, investment advice, or a substitute for professional review.
Who it supports
brokerages, teams, and real-estate operators
Comparable sales, buyer education, listing research, neighbourhood comparison, and market-report support for professional real-estate teams.
Open use casejournalists, editors, newsletters, commentators, and research teams
Turn local property data into clearer housing explainers, affordability stories, market movement summaries, and media-ready context.
Open use casedevelopers, AI-agent builders, SaaS teams, and proptech platforms
MCP and integration support for teams exploring AI-native Ontario property research.
Open use caseinvestors, analysts, operators, and acquisition or research teams
Rental comparables, price support, inventory movement, area comparison, and property-type analysis for professional research tasks.
Open use caseData matrix
agent0 is strongest when the site clearly separates data categories, what they are used for, and where human review still matters.
Used for
Active options, shortlist candidates, buyer briefs, listing context
Caveat
Visibility depends on access level, source rules, and current availability.
Used for
Price support, CMA support, offer context, market interpretation
Caveat
Use only where available and permitted; review dates and similarity carefully.
Used for
Rent support, landlord research, investor screening, affordability notes
Caveat
Rental data can vary by source, timing, building, parking, and condition.
Used for
Inventory movement, price-band context, property-type comparisons
Caveat
Statistics should be treated as research context, not guaranteed forecasts.
Used for
Area comparisons, local trade-offs, media explainers, buyer education
Caveat
Local interpretation still needs professional and source-level review.
Used for
Condo research, property notes, caveats, follow-up questions
Caveat
Attribute completeness varies by record, source, and property type.
Controlled access
agent0 is designed for approved professional use cases, not unrestricted data redistribution. Access, data visibility, and output expectations are reviewed before deeper use.
Founder perspective
agent0 was created by Arunan Kuven, an Ontario real-estate broker and operator, to make serious property research more usable inside AI agents. The product reflects a practical brokerage view: outputs should be structured, caveated, access-aware, and reviewable by a person before they are relied on.
FAQ
agent0 is an Ontario real-estate MCP server that connects approved AI workflows to structured property intelligence, including listings, sold comparables, rental context, market statistics, neighbourhood data, and research-ready outputs.
No. agent0 is positioned as property-intelligence infrastructure for professional AI use cases, brokerages, analysts, researchers, media teams, and proptech builders.
ChatGPT and Codex are available through reviewed onboarding. Claude and Claude Code are planned surfaces. MCP access is reviewed by use case, data need, and access requirements.
No. agent0 produces research support and human-reviewable outputs. Important legal, mortgage, tax, appraisal, investment, or client-advice decisions require independent professional review.
Request a focused walkthrough for comps, market briefs, buyer reports, rental context, neighbourhood comparisons, shortlists, alerts, CMA support, or research-ready outputs.