25 purpose-built tools
20M+ properties, 190+ countries
One-command setup
$ claude mcp add --transport http airroi \ https://mcp.airroi.com \ --header "X-API-KEY: YOUR_API_KEY"
Properties Tracked
Countries Covered
Years of History
MCP Tools
API Uptime
AirROI's MCP server uses Anthropic's Model Context Protocol — the open standard adopted by Google, OpenAI, and Microsoft — to let any compatible AI client discover and call 25 STR analytics tools automatically. Here's how you go from zero to querying 20M+ properties in three steps.
1
Sign up at AirROI and get an API key from the Developer Dashboard in under a minute. No sales calls, no approval process, no enterprise contracts. Pay-as-you-go pricing starts at just $10 in credits, so you can start small and scale as your needs grow.
2
Run a single command to connect AirROI to your AI client. The setup works with Claude Code, Claude Desktop, Codex, Cursor, VS Code, and any MCP-compatible client. No SDK installation, no dependency management, no configuration files to edit.
3
Start querying STR data naturally. Ask “What’s the average occupancy in Miami Beach?” or “Compare RevPAR for 2BR listings in Austin vs Nashville.” Your AI selects the right tool, calls it with the correct parameters, and formats the results for you.
Your AI becomes a full-powered STR analyst. Watch a real session — from market discovery to investment-ready analysis in under 60 seconds.
> I'm thinking about buying a vacation rental in Joshua Tree. Is the market saturated? What can a 2BR realistically earn?
Joshua Tree, CA — Market Overview Active Listings: 1,847 (YoY: +6.2% supply growth) Avg Occupancy (2BR): 61.8% Avg ADR (2BR): $312/night Avg RevPAR (2BR): $193 Median Annual Revenue: $68,400 Seasonality: Peak (Oct-Dec): 78.3% occ · $389 ADR Shoulder (Jan-Apr): 64.1% occ · $318 ADR Low (Jun-Aug): 42.7% occ · $241 ADR (desert heat)
The market is growing but not saturated — 6.2% supply growth is moderate. Summer is the weak spot (desert heat tanks occupancy to 42%). A 2BR can realistically gross $60-75K/yr, with top performers hitting $90K+ through premium amenities and dynamic pricing.
> Show me top-performing 2BR comps with a hot tub. What are the best ones earning and what makes them stand out?
Top 5 Comps — 2BR w/ Hot Tub, Joshua Tree # Revenue Occ ADR Rating Reviews 1 $104,200 74.1% $385 4.97 312 2 $92,800 71.8% $354 4.94 267 3 $87,100 69.2% $345 4.91 198 4 $79,500 66.5% $327 4.89 241 5 $74,300 64.3% $316 4.85 184 Top Performer Avg: $87,580/yr ADR: $345 Occ: 69.2%
The top earner ($104K) stands out with 4.97 stars, 312 reviews, and a $385 ADR — likely premium design + outdoor living space. All top 5 have hot tubs, outdoor fire pits, and stargazing decks. Pattern: properties above $80K/yr consistently have 4.9+ ratings and 180+ reviews. Investing in guest experience pays directly into ADR.
The AI chained 5 tool calls across market analytics, revenue estimation, and comp analysis — synthesizing an investment-ready brief that would take a human analyst hours of dashboard work.
MCP (Model Context Protocol) is the open standard created by Anthropic for connecting AI models to external data sources. Google, OpenAI, and Microsoft have all adopted it, making it the de facto universal protocol for AI tool integration. AirROI's MCP server works with any client that supports the standard.
Anthropic's AI coding agent for terminal workflows
Anthropic's desktop app — no terminal or coding required
OpenAI's coding agent: ChatGPT app, CLI, and IDE extension
AI-first code editor with built-in MCP support
GitHub Copilot with MCP support in VS Code
Windsurf, Gemini CLI, and any MCP-compatible client
Unlike generic MCP servers that offer 2–4 basic tools returning raw page data, AirROI provides 20 analytics-enriched tools purpose-built for short-term rental data. Every tool returns pre-computed metrics — occupancy rates, ADR, RevPAR, revenue estimates, booking patterns, and more — so your AI can deliver actionable insights without post-processing.
Calculate a base price and nightly recommendations with explanations and warnings. Both tools use your existing API key and credits. Calendar results include the complete response inline; no property settings are changed or rates published.
recommend_base_price
Property reference price, alternatives, currency, and itemized explanationrecommend_calendar_prices
Nightly prices, explanations and coverage inline; optional start_date and end_date, full one- to two-year defaultAccess property-level data for 20M+ short-term rental listings worldwide. Retrieve comprehensive details, historical performance metrics spanning up to 60 months, future rate calendars extending 365 days out, and run multi-mode searches by market, radius, or custom polygon boundaries. Batch operations let you analyze up to 25 listings in a single call, or bulk export an entire selection to a downloadable JSONL or CSV file (delivered as a presigned link) for offline analysis.
get_listing
Comprehensive property data: amenities, reviews, pricing, and real-time performance analyticsbatch_listings
Fetch up to 25 properties simultaneously for portfolio analysis and bulk comparisonsfind_comparables
Discover similar properties by location and size for competitive pricing benchmarkslisting_metrics
Up to 60 months of historical occupancy, ADR, revenue, and booking pattern datalisting_future_rates
365 days of forward-looking nightly rates, availability, and minimum stay rulessearch_by_market
Search listings within specific cities or neighborhoods with advanced filterssearch_by_radius
Find all properties within a specified radius from any GPS coordinatessearch_by_polygon
Search within custom geographic boundaries for precise market segmentationexport
Bulk export every matching listing (market, radius, or polygon) to a downloadable JSONL or CSV file via a presigned linkDeep market intelligence for STR markets globally. Track occupancy rates, average daily rates (ADR), revenue per available rental (RevPAR), total revenue, booking lead times, length of stay patterns, supply growth, and forward-looking pacing data. Market tools draw from 15+ years of historical data and deliver percentile breakdowns (average, p25, p50, p75, p90) so you understand not just the average but the full distribution.
market_summary
Comprehensive market overview with listing count, KPIs, and investment potentialmarket_metrics_all
Complete analytics dataset: occupancy, ADR, RevPAR, revenue, and booking patternsmarket_occupancy
Historical and seasonal occupancy rate trends with percentile breakdownsmarket_adr
Pricing trends and seasonal ADR fluctuations across the entire marketmarket_revpar
Revenue per available rental combining occupancy and pricing performancemarket_revenue
Total market revenue generation and growth trends for investment analysismarket_lead_time
How far in advance guests book to optimize pricing and marketing timingmarket_los
Average guest stay duration patterns for operational and pricing optimizationmarket_min_nights
Monthly minimum-night settings with average and percentile breakdownsmarket_active_listings
Market supply growth, new entrants, and competitive landscape changes over timemarket_future_pacing
Forward-looking occupancy based on current bookings for demand predictionMarket discovery and revenue estimation tools for investment analysis and property valuation. Search and discover markets by name across the global hierarchy (country, state, city, neighborhood), resolve GPS coordinates to the right market, or generate ML-powered revenue projections for any property based on location, size, and comparable listings in the area.
search_markets
Discover markets by name with hierarchy for use in other market toolslookup_market
Resolve GPS coordinates to the exact market identifiers needed by other market toolsestimate_revenue
ML-powered revenue projections based on location, size, and comparable propertiesUse both pricing tools through your existing AirROI MCP connection and API key. These tools calculate recommended prices; listing_future_rates shows observed listing rates, and estimate_revenue projects earnings.
airroi_recommend_base_priceEstimate a year-round nightly starting price from property coordinates and known property facts. Optional amenities, reviews, and other details can refine the estimate. Nothing is saved or published.
Try asking: Recommend a base price in USD for a 2-bedroom, 2-bath property sleeping 4 at latitude 25.7907, longitude -80.13. Explain the recommendation.
Returns: Recommended base price, conservative/balanced/aggressive options, typical local price range, returned currency, and itemized explanation
View API contractairroi_recommend_calendar_pricesCalculate nightly prices from coordinates, currency, and a base price. Modeled seasonality, weekdays, events, and demand combine with optional pricing rules, seasonal overrides, price limits, and stay restrictions.
Try asking: Use the returned base price and currency to recommend nightly rates for the same property with balanced last-minute pricing. Summarize the next 30 days and all warnings; do not publish anything.
Returns: The complete response inline: nightly recommendations, explanations, stay restrictions, coverage, and warnings. Omitted or null start_date and end_date return one to two years of dates from today in the property timezone; inclusive boundaries select a window
View API contractProvide exact coordinates, bedrooms, bathrooms, and guest capacity to recommend_base_price. Omit optional property facts you do not know.
Pass recommended_base_price and the returned uppercase currency to recommend_calendar_prices, or supply your own base price and currency. Currency uses codes such as USD or JPY; the analytics option native does not apply.
Add pricing rules and availability evidence when needed. The calendar is booking evidence, not an output date selector. Missing dates are unknown; include surrounding reservations and blocked dates for calendar-dependent rules.
Review explanations and warnings, including warnings on successful responses, before publishing through your own integration.
Calendar Prices returns the complete response inline with nightly prices, explanations, stay restrictions, coverage, and all warnings. Output starts today in the property's timezone unless start_date is set. Omitted or null start_date and end_date return the full one- to two-year calendar; inclusive boundaries select a window. The same day in both returns one row. Past or malformed dates fail; an end_date beyond available coverage returns available rows with a warning. Output selection preserves full-calendar calculations and warnings, whose counts may include later dates. Review warnings even on success. The calculation is stateless: resend settings and calendar evidence; no property settings are changed or rates published. Your booking integration must enforce returned stay restrictions. Length-of-stay discounts are accepted but are not applied to recommendations.
Each tool uses the same paid credits as its REST endpoint. Calendar Prices is billed per successful request, not per night. Check current rates on the API pricing page.
AirROI is the most dedicated platform built specifically for Airbnb and short-term rental analytics. While generic MCP servers like Bright Data, OpenBnB, and Apify cover many verticals with a handful of basic tools, we've invested years into building a purpose-built STR database with 20M+ properties, 15+ years of history, and pre-computed analytics like occupancy, ADR, RevPAR, and revenue breakdowns. That depth and specialization means higher data quality, richer metrics, and insights you simply can't get from generic alternatives.
| Feature | AirROI MCP | Generic MCP Servers |
|---|---|---|
| Purpose-built STR tools | 25 | 2–4 generic |
| Market analytics (occupancy, ADR, RevPAR) | ||
| Revenue estimation | ||
| Data depth | 15+ years, percentile breakdowns (p25–p90) | None or surface-level |
| Forward-looking data | Future rates (365 days) and booking pacing | None |
| Global coverage | 190+ countries, 20M+ properties | Limited |
| Data type | Analytics and raw data | Raw page data |
| Compliance | Authorized API, fully ToS-compliant | Scraping risks ToS violations and IP bans |
| Reliability | Managed infrastructure, consistent uptime | Fragile scrapers, frequent breakage |
| Setup | One command | Medium complexity |
See how investors, property managers, and developers are using the AirROI MCP server to build smarter workflows and better proptech products.
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“I used to spend the first two hours of every Monday pulling comp rates and occupancy trends from three different dashboards. Now I ask Claude one question through the MCP server and get a formatted competitive analysis before my coffee gets cold. My pricing recommendations are faster and more data-backed than ever.”
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“Integrating the AirROI MCP server into our internal Slack bot was a game changer. Our property managers can now pull up comp sets and live market occupancy directly from their phones while doing property walk-throughs, without needing to learn complex dashboards.”
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“As a developer building a custom pricing tool, I didn't want to spend months wrangling raw API data and handling pagination. The MCP server gave my AI agent instant access to pre-computed RevPAR and seasonal pacing data. What would have taken weeks to build took me a weekend.”
Everything you need to get started with AirROI's MCP server — from one-command setup to real-world use cases across investment analysis, property management, and proptech development.
Yes. Use airroi_recommend_base_price with coordinates and property facts, then pass its recommended_base_price and returned currency to airroi_recommend_calendar_prices. You can also supply your own base price. Calendar Prices returns the complete response inline with explanations, stay restrictions, coverage, and all warnings. Omitted or null start_date and end_date return one to two years of dates from today in the property timezone; inclusive boundaries select a window. The same day in both returns one row, past dates fail, and an end_date beyond coverage produces a warning. Review warnings even on success. Calculations are stateless: no property settings are changed or rates published. They consume the same paid credits as the REST endpoints; Calendar Prices is billed per successful request, not per night.
An MCP (Model Context Protocol) server is an open standard that lets AI assistants like Claude, ChatGPT, and Cursor connect to external data sources through a universal interface. Think of MCP as USB-C for AI — one protocol to connect any AI model to any data source. AirROI's MCP server exposes 25 STR analytics tools through this protocol, so your AI can query Airbnb market data, revenue estimates, and property analytics via natural language.
Generic MCP servers like Bright Data, OpenBnB, and Apify offer a handful of basic tools that return raw page data without analytics. AirROI is purpose-built for STR intelligence — 25 specialized tools delivering pre-computed metrics like occupancy rates, ADR, RevPAR, revenue estimates, and 15+ years of historical data across 20M+ properties in 190+ countries. It’s the difference between raw ingredients and a finished meal.
REST APIs require developers to write code against fixed endpoints. MCP servers are designed for AI clients — the AI model discovers available tools, selects the right one based on your natural language question, and executes it automatically. AirROI offers both: developers can use our REST API directly, while AI users can connect via MCP for a code-free experience. Both consume the same pay-as-you-go credits.
Same pay-as-you-go pricing as the REST API. No subscriptions, no contracts, no monthly minimums. Buy API credits starting at $10, and each MCP tool call consumes the same credits as the equivalent REST API call. See the pricing page for detailed per-call costs.
Any client supporting the Model Context Protocol: Claude Code, Claude Desktop, OpenAI Codex (ChatGPT desktop app, CLI, and IDE extension), Cursor, VS Code with Copilot, Windsurf, and more. MCP is an open standard created by Anthropic and adopted by OpenAI, Google, and Microsoft. The ecosystem is growing rapidly, with new clients adding MCP support regularly.
No. If you can install Claude Desktop, you can use the MCP server. The one-command setup takes 30 seconds, and then you query data in plain English — no coding, no API integration, no data processing required. Ask questions like “What’s the average occupancy rate in Miami Beach?” and get structured analytics back instantly.
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