Use cases

Skai Connectors + Your Agent. Any use case, done efficiently.

Your setup

  • Reporting Connector — always available. Setup guide
  • Operations Connector — available for this channel. Setup guide
  • Publishers — Google, Meta, Amazon, and Walmart (and 300+ publishers globally).
  • Savings — in testing, about half the tool calls of calling publisher MCPs directly. Full breakdown

Flows

Find the leak, then fix it

From a ranked list of waste to an applied budget cut, without leaving the conversation.

  1. 1

    Spot it

    Reporting MCP
    Across Google, Meta, Amazon, and Walmart, which campaigns spent the most last week with ROAS below my account average? Show spend, conversions, and ROAS.

    A ranked table with the worst offenders at the top.

  2. 2

    Diagnose

    Reporting MCP
    Take the worst one. Compare it to the prior week and tell me whether the drop came from spend mix, CPCs, or conversion rate.

    The driver behind the change, not just the delta.

  3. 3

    Check the history

    Reporting MCP
    What changed on that campaign in the last 14 days, and who changed it?

    Every edit, who made it, and the before and after values.

  4. 4

    Stage the fix

    Operations MCP
    Cut its daily budget 20%. Show me the preview before anything is applied.

    A staged change you approve or reject. Nothing applies without you.

  5. 5

    Put the money to work

    Operations MCP
    Which Google, Meta, Amazon, and Walmart campaigns are efficient but budget-constrained? Stage a 15% increase on the top three.

    Three staged increases, each with the evidence behind it.

Trust the numbers before you build on them

From what's queryable to a clean extract you'd stake a dashboard on.

  1. 1

    Discover

    Reporting MCP
    What can you query for Google, Meta, Amazon, and Walmart? Show me the fields available at campaign and keyword level, and which group each belongs to.

    The real column set, discovered from the account rather than guessed.

  2. 2

    Audit

    Reporting MCP
    Check the last 30 days for zero-spend days, missing revenue, and any campaign reporting conversions with no revenue.

    The gaps worth knowing before you trust a number.

  3. 3

    Pin the definitions

    Reporting MCP
    How is ROAS calculated in this data, and does a conversion mean the same thing on Google as on the rest?

    What each metric actually counts, before you build on it.

  4. 4

    Reconcile

    Reporting MCP
    My Google spend doesn't match my BI numbers for last month. Pull the daily series so I can diff it.

    A daily series you can line up against your own source of truth.

  5. 5

    Extract

    Reporting MCP
    Give me campaign-level spend, clicks, conversions, and revenue for Google, Meta, Amazon, and Walmart, last 90 days, daily, structured so I can drop it into a sheet.

    A structured extract, no reshaping needed.

One screen to a shared priority list

The whole account, then the one thing worth acting on, then something you can send.

  1. 1

    See it all

    Reporting MCP
    Give me a one-screen view of Google, Meta, Amazon, and Walmart this month versus last — spend, revenue, ROAS, and the three biggest movers.

    The whole picture on one canvas, movers called out.

  2. 2

    Steer

    Reporting MCP
    Drill into whichever publisher moved most and show me what's underneath it.

    Top-down first, depth exactly where it matters.

  3. 3

    Test the hypothesis

    Reporting MCP
    I think we're over-invested in Google branded terms. Show me the data that would confirm or kill that.

    The evidence both ways, not agreement.

  4. 4

    Quantify it

    Reporting MCP
    What's our incremental ROAS across Google, Meta, Amazon, and Walmart last quarter, and where is it weakest?

    iROAS by publisher, with the weak spots ranked.

  5. 5

    Make it shareable

    Reporting MCP
    Build the ten campaigns with the most wasted spend last quarter, one line of reasoning each, that I can send to the team.

    A list that stands on its own in someone else's inbox.

Use cases

Explore different use cases where Skai's connectors help your agents work faster, smarter, and more efficiently.

  • Underperformer scan

    Skai's Reporting MCP connector hands your agent the account's real columns and normalized spend, conversion, and ROAS data across every connected publisher — no guessing at schema, no five different export formats to reconcile first. Your agent goes straight to ranking the worst offenders instead of spending its effort just assembling a clean dataset.

  • Budget-cap check

    The Reporting MCP connector surfaces each campaign's daily budget alongside its performance, so your agent can flag anything hitting its ceiling while still converting above average in one request. That's a cross-platform check your agent couldn't do quickly on its own without a connector already speaking every publisher's schema.

  • Zero-conversion audit

    Skai's connector returns spend and revenue in the same structured pull, so your agent can catch campaigns reporting cost with no matching revenue before that gap quietly breaks a ROAS calculation downstream. Clean, joined data up front is what makes the catch possible in one pass instead of several.

  • Publisher efficiency comparison

    The real efficiency here is the connector, not the ranking — Skai's Reporting MCP normalizes the same metric set across every publisher into one schema, so your agent compares apples to apples instead of reconciling column names platform by platform. In head-to-head testing against calling those publisher MCPs directly for the same questions, that normalization needed about half the tool calls and ran roughly thirty percent faster — the toolchain work Skai does so your agent doesn't have to.

  • Ad-type / placement mix audit

    For retail media accounts, the connector breaks spend down by sponsored products, sponsored brands, and display in the shape your agent needs, instead of requiring separate exports merged by hand. Your agent spends its turn on the "which format is pulling its weight" question, not on data wrangling.

Anatomy of a good prompt

  1. 1

    Name the entity

    campaign, keyword, product.

    How are things?

    How did my campaigns do?

  2. 2

    Name the metric

    spend, ROAS, CPA, conversions. Otherwise you get a dump instead of an answer.

    How did my campaigns do?

    How did my campaigns do on spend and ROAS?

  3. 3

    Bound the window

    "last week", "this quarter versus last".

    How did my campaigns do on spend and ROAS?

    How did my campaigns do on spend and ROAS last week?

  4. 4

    State the decision

    "...and what should I cut?" turns a report into a recommendation.

    How did my campaigns do on spend and ROAS last week?

    How did my campaigns do on spend and ROAS last week, and what should I cut?

The agent leads with the answer and shows the numbers behind it, so asking for the decision usually beats asking for the data.

Learn more

Are you already a Skai client?

One connector, every publisher — more complete answers, lower cost

No single publisher's MCP can answer a cross-publisher question, no matter how efficient it gets. Here's what we measured on top of that.

~2.5x more complete
Real, data-backed answers
810-run, 3-judge battery against a 100+-tool publisher MCP
~2x fewer
Tool calls per query
9.6 vs. 18.9, averaged across five common reporting workflows
1.5x–8x cheaper
Cost per query, depending on publisher MCP size
$0.34 vs. $0.59 against small publisher schemas; up to 8x against a 100+-tool publisher MCP

See the full measured and modeled breakdown, plus completeness and unattended-operation findings →

See it for yourself

Run these same questions against the live demo.

Try it yourself