Today, we’re sharing the Wheelhouse Plugin Library — a public, growing collection of Revenue Management skills you can connect straight into your AI client.

If you’re already using the Wheelhouse MCP, you know it gives your agent direct access to your portfolio — listings, pricing, reservations, preferences, and more.

The Plugin Library goes a step further: it packages that MCP connection together with the actual workflows our Revenue Management team builds, so your agents can do real analysis, faster & easier.

A few of the skills already in the library:

You don’t have to name a skill or remember which one to use — ask in plain English and your agent picks the right one:

Under the hood, we’ve also made a real efficiency improvement to how the Wheelhouse MCP handles complex, data-heavy requests.

Previously, when your agent needed to work through a lot of data, it worked one step at a time: call a tool, wait for the full result to come back through the model, decide what to do next, call the next tool. Every one of those round trips costs tokens and time.

Now, for multi-step, data-heavy requests, your agent can write a short piece of code that calls the tools it needs, loops through the results, and filters everything down — all before anything comes back into its context. Instead of pulling every reservation or every day’s pricing data through the model just to produce a summary, only the answer you actually asked for makes the trip.

As an example, this enabled us to reduce pulling reservations by 96%!

Connecting is a one-step install, and our Plugin Library is supported everywhere: Claude Code, Cursor, Codex, Grok Build, and ChatGPT (Enterprise/Work, via admin-managed import) can all run the library.

And, the plugin library updates automatically as we ship new changes — there is nothing to reinstall/update/etc.

Here’s how easy it is in Claude Code, as an example — two lines, pointing at one repo:

See a full walkthrough of connecting it in Claude here:

Instructions for Cursor, Codex, Grok Build, and ChatGPT are linked at the bottom of this post.

And, for more, you can always review our API docs:

🎥

🩻

Your Pricing Chart just got much more robust. It now features 43 series, which includes major new series such as the:

Your series are now organized into 9 categories — Pricing Series, Pricing Modifiers, Limits & Controls, Bookings & Blockings, Your Settings, Pricing Engine Breakdown, Set Data, Neighborhood Data, and Market Data — each expandable, each with its own set of series you can turn on or off. You control exactly how much (or how little) is on screen at once.

⚙️

We made it much easier to compare each of your settings to the Pricing Engine Recommendations.

For example, if you have a custom Seasonality setting, you can easily see how it compares to our Pricing Engine's current Seasonal Recommendation.

We think this view will be very helpful both now, and when evaluating or updating to future Pricing Engine versions (e.g. 9.1, 10.0).

📊

Comparing your price to comps just got more useful. Neighborhood Data now includes Occupancy and Occupancy (Adj.), not just price median and range — so you can see whether a market is actually filling up, not just what it's asking. Set Data (your Dynamic Sets) gets the same Occupancy additions, and Market Data now supports Occupancy alongside its existing Median and Range series.

🧮

A few series were renamed so the label matches what the number actually is:

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19 series have been renamed, but the data is unchanged.

We renamed these series to make it easier to find & leverage the right data sets for you.

Often, we made Series names more explicit. Sometimes, we shortened them, and moved data to the descriptions you see when you hover on a series name.

The full list of updated names is here:

TL;DR →

For a while, understanding daily performance for a group of listings required heading to your Performance page.

Now, we're bringing daily performance information where you need it - right to the top of your Portfolio Calendar. That means you can easily see:

Hover over any value for a full definition — no more guessing which flavor of a metric you're looking at.

Here's where it gets useful at scale.

Whatever slice of your portfolio you're looking at, the aggregate performance numbers for that slice are already sitting at the top of the calendar.

Recently, we launched many new metrics, so you can better unpack how any listing is performing.

However, another important view is how performance is rolling up to a daily level.

Adding these metrics to your calendar view is step one of helping you think about pricing strategies tied to how each day is performing... not just each listing.

For teams managing large portfolios, that means faster triage, without ever leaving the calendar.

Aggregate Performance Rows are on by default in your Portfolio Calendar. To show or hide them:

As noted in the intro, due to data loading, we currently ONLY show the aggregated rows for views that contain .

📽️ Link here

🌙

We've refined how the More conservative option handles last-minute pricing. The updated curve applies deeper discounts as arrival gets closer, then tapers those discounts more gradually the farther out you go — reflecting real-world discounting patterns we've seen across operators.

🔭

The More conservative option for Far Future Premium got the same treatment. Previously, it didn't start adding a meaningful premium until roughly 365 days before arrival. The new curve starts building a modest premium around , gradually increasing toward approximately the farther into the future you price.

🛡️

If one or more of your listings were already using More conservative for either setting, we didn't flip you onto the new curve. Instead, we moved those listings to a that closely matches the More conservative curve — so the pricing behavior you were already relying on stayed consistent.

In other words: improving the data-driven option doesn't mean your live strategy changed out from under you. If you did nothing, you're exactly where you were.

🚀

No action is needed if you're happy with how your pricing has been working. If the new curve sounds like a better fit for your listing(s):

🥳

Not sure whether the new curve or your current Custom rule is the better fit? Try switching one listing over first and compare pacing over the next few weeks before rolling it out portfolio-wide.

Questions? Reach out to our 24/7 live chat — we're happy to walk through it.

Yes.

It is for us to endorse tech platforms for our users.

However, having spent time with the Wander team, in our opinion, this platform has reached the point that it is well worth highlighting to you.

So, why learn about Wander?

Our hot takes...

Learn a LOT more about Wander here.

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Segments are your saved, dynamic portfolio filters — bedroom count, market, tags, occupancy thresholds, whatever criteria matters to how you organize your portfolio.

The RM API and MCP already let you list your segments and pull the listings each one matches.

Now you can also pull the rolled-up performance for the whole segment, in one call or chat.

returns one row per month, covering:

By default, the response covers every period Wheelhouse has data for — a segment built today can still return months of trailing history for the listings it matches. Pass one or more to restrict the response to specific months (send as , always the first of the month).

Say you manage a segment called "4BR+ Mountain Cabins" — 40 listings across three markets, grouped by a bedroom-count filter. Instead of pulling listing-level performance data for all 40 and aggregating it yourself, you can now call the new endpoint once and get:

That's the exact math Wheelhouse runs when you view that segment's performance chart in the app — now yours to pull into a dashboard, an owner report, or an internal alert without re-deriving it.

You don't have to write any code to use this. If you've connected an AI assistant to Wheelhouse via MCP, you can just ask for it in plain English.

Under the hood, that's the same aggregated_metrics endpoint — the assistant calls it, then reasons over the monthly numbers to answer the actual question you asked, instead of you scanning a table yourself.

Ask it to compare two segments, flag which months are underperforming last year, or summarize a segment's trend for an owner update, and it'll pull the data and do the synthesis in one step.

In short, direct integrations get the raw monthly rows, and MCP-connected assistants turn those rows into an answer.

Segments are flexible by design — you can build one around almost any combination of listing attributes, tags, or performance thresholds. That flexibility is most useful when you can act on it programmatically, and aggregation was the missing piece.

Before this release, getting a segment-level number meant pulling every matched listing's data and rolling it up client-side — doable, but it meant re-implementing logic Wheelhouse already runs.

Now the aggregation lives on our side. Combine this with the existing and endpoints and you have the full loop: define the filter, see what it matches, and get the rolled-up numbers for it — all through the API.

And, with your MCP up-to-date, all you need to do is ask.

"Can you tell me how my Segment "New Owner, Occupancy Pacing Flag" is performing?"

Two parameters worth knowing about:

The endpoint is live now under your existing RM API key — no new authentication or setup required.

If you're connecting through an MCP client, authenticate with OAuth through your client's sign-in flow — the same credentials you use in the Wheelhouse app.

🥳 Loop this endpoint across every segment returned by to build a lightweight performance dashboard that stays current as your segment filters (and the listings they match) change — no manual re-aggregation, ever.

📽️ Watch a demo — [TODO: confirm if a video walkthrough exists for this release]

TL;DR →

Until now, keeping your system in sync with Wheelhouse meant polling — calling an endpoint on a schedule and diffing the results to see what changed. Webhooks flip that: you register an HTTPS endpoint once, and Wheelhouse pushes events to it as they happen.

Setting one up is a single call: give us a URL and the event types you want, and you're covered. One subscription spans your entire portfolio — every listing you own plus every listing shared with you to manage — so there's no per-listing setup, and no need to maintain multiple subscriptions as your portfolio grows.

Every event at launch is scoped to a listing:

All three are batched: instead of firing the instant something happens, events collect for 5 minutes, then go out together. So if pickup on 40 listings triggers within the same few minutes, you get one delivery covering all 40 — not 40 separate webhook calls to process.

A few things this actually looks like:

When you create a subscription, the response includes a signing secret — and that's the only time you'll see it. Store it immediately; it can't be read back later. If you lose it, or it leaks, rotate it and you'll get a new one on the spot. Just know the old secret stops working the moment you rotate, so have your endpoint ready to verify against the new one before you do.

Every delivery attempt is logged — the HTTP status your endpoint returned, and the reason for any failure — retained for 7 days and pullable up to 200 at a time. If your endpoint starts timing out or rejecting deliveries, this is where you go to see exactly what Wheelhouse sent and how your side responded.

And if your endpoint goes down for a while, Wheelhouse won't keep hammering it forever: a subscription that fails repeatedly pauses itself automatically. Once your endpoint's healthy again, re-enable the subscription and the failure count clears — it starts fresh rather than counting against you from before the fix.

You're also in control directly: pause a subscription with any time you need to do maintenance on your end, and resume it the same way when you're ready.

Wire a webhook into your workflow, and pair it with a Scoreboards call the moment it fires. The webhook tells you a listing just got flagged — Scoreboards tells you where that listing actually ranks against the rest of your portfolio on the metric that matters, so you're not reacting to one signal in isolation.

📽️ Demo here:

TL;DR →

A few weeks ago, Wheelhouse added dozens of new performance stats.

And, with our RM API live, we wanted to make it easier to analyze your whole portfolio quickly, instead of pulling all data for your listings.

Therefore, we built a new endpoint we call "Scoreboards", so you send one request telling us which stat you care about, and we send back every listing you manage, ranked from best to worst on that stat — in seconds.

Because it's part of our API, you can plug the results you get from Scoreboard straight into whatever tools you already use.

For example: pull the listings losing the most money to blocked dates, and automatically email their owners. Or pull the premium listings that haven't booked in a week, and flag them for a price change.

Before Scoreboards, getting one stat across your whole portfolio meant looking it up one listing at a time, then doing the ranking yourself. That's fine for 5 listings. It's painful for 500.

Scoreboards does the ranking for you. You tell it which stat and which time period you want, and it hands back every listing you manage, already sorted from best to worst — in one request.

Learn more in this 4 minute video:

When you make the request, you only need to tell us two things: which stat you want (we call this the ), and what time period to look at (the ).

There are more than 30 stats to choose from. Roughly, they fall into a few buckets:

For the time period, you can look forward or backward — anywhere from the next/last 7 days out to a full year.

For API calls, you will get an array back you can easily sort. And, for chats (Claude, Open, Gemini) you can simply ask for a "top 5" or "worst 10" — to return a specific number of listings.

Additionally, you can easily filter this list - narrowing your request down to one market, one bedroom count, or anything else you track, on your end.

If your listings are priced in more than one currency, Scoreboards can convert dollar-based stats (like ADR or revenue) into a single currency before ranking, so you're not comparing apples to oranges.

You can access Scoreboard via our MCP as well, enabling you to more quickly analyze your portfolio.

Your LLMs will use Scoreboards and answers in seconds.

We think this looks like the foundation of a better workflow: use Scoreboards to zoom out and find the listings that need attention, then zoom in to fix the ones that need it.

Once Scoreboards has narrowed your portfolio down to a handful of listings worth a closer look, pull each listings full stats history to understand exactly what's going on before you make a change.

You'll find more in our documentation, but for some quick hits, here's how you use this endpoint.

Full metric list:

Ask for a metric/window pairing that isn't valid (e.g. with a window) and you'll get back a rather than a bad number.

A listing with no data for the requested window still shows up, with , so it sorts to the bottom instead of disappearing.

A listing with no stats generated at all is left out entirely.

Wheelhouse handles the revenue management side of your business — but Airbnb's algorithm is weighing a lot more than your nightly rate when deciding who shows up on page one.

Your listing title, photo quality, description strength, review velocity, and availability configuration all factor into how Airbnb ranks and distributes your property to potential guests. A perfectly priced listing that ranks poorly is leaving money on the table — and most of those ranking signals are fixable once you know where the gaps are.

That's the problem AutoRank by Otamiser was built to solve. Their tool audits the listing-level factors that influence your Airbnb ranking, and we've partnered to make it available to Wheelhouse users at no cost.

Drop in any Airbnb URL and the tool evaluates:

You walk away with a clear, scored breakdown of where your listing profile is strong and where it has room to improve.

Getting started takes about 60 seconds (Video proof here!)

No signup required to run the audit. It's fully standalone and free.

If you're managing a large portfolio, start with your top-revenue properties or those in your most competitive markets. That's where small ranking improvements compound fastest — and where a stronger listing profile has the most to add on top of your pricing strategy.

Dynamic Sets (comp sets built from real market listings) have been available in the app for a while — now you can build the entire workflow through the API, from search to upgrade.

Here's the full lifecycle:

Say you manage a portfolio of 3BR cabins near a specific trailhead and want a comp set for one of them. You can now search candidates by lat/long/radius, filter to , review trailing-year occupancy on the results, add the ones you want, and upgrade the set — all without leaving your integration.

Segments (your saved, dynamic portfolio filters) already return listing-level data via the API. Now you can also pull rolled-up performance for the whole segment.

returns monthly metrics aggregated across every listing the segment's filter currently matches.

This is the same aggregated view you'd see reporting on that segment in-app — now available directly to your integration, so you can more easily build dashboards or alerts on top of any segment.

You can now read and manage a user's alert preferences without going through the app:

Supported events include:

If you're building a custom notification center or want to route specific alerts to your own system, this is the endpoint pair for that.

We've raised the default rate limit for RM API keys to . If you're running higher-throughput integrations — syncing large portfolios, polling multiple endpoints, or backfilling historical data — this gives you meaningfully more headroom out of the box.

If you're using the RM API directly, generate or check your API key from your Wheelhouse account under "Api Key," and start with or to see what's available for your account.

If you're connecting through an MCP client, authenticate with OAuth through your client's sign-in flow — the same credentials you use in the Wheelhouse app.

🥳 Pair the new endpoint with your existing Segments to build a lightweight performance dashboard for any saved filter — no need to pull and re-aggregate listing-level data yourself.