Ads and traffic through MCP: Meta Ads and Google Analytics without exports

Pull the performance of ad account [account] for the period
[July 1-31, 2026]. Table by campaign, sorted by spend
descending: campaign | objective | spend | impressions |
clicks | CTR | CPC | results | cost per result

Below the table: total for the account, the three campaigns
with the best cost per result and the three with the worst,
and which campaigns have so few results that their cost per
result doesn't mean anything yet.

State the period and currency used. Don't estimate anything —
leave a missing metric blank and say why.

Compare the performance of ad account [account] for the
period [July 1-31] against [June 1-30].

Table by campaign: metric, value in the first period, value
in the second, absolute difference, percent difference.
Metrics: spend, impressions, reach, clicks, CTR, CPC, results,
cost per result.

Then list:
1. The three biggest declines in absolute money, not
   percentage — where the change cost us the most. And the
   three biggest improvements.
2. Campaigns that didn't run the full length of either
   period — comparisons for those are misleading, flag them.
3. Changes that are most likely just noise, not a trend, and
   why.

Don't give recommendations, just describe. I want
recommendations after this.

Go through ad account [account] for the last [30] days at the
ad-set level and find where budget is being burned without
results. List ad sets that meet at least one of:
- spent more than [2,000 Kč] and have zero results
- have a cost per result more than [double] the account
  average
- have a CTR below [half] the account average while spending
  over [1,000 Kč]

For each, give: campaign, ad set name, spend, results, cost
per result, CTR, how long it's been running, and which
condition applies.

Sort by how much money is at risk per month, and at the end
give the total amount that's leaking away monthly this way.

Don't turn anything off or propose specific budget changes —
just a list for me to decide on.

Pull the frequency trend for ad account [account] over the
last [60] days, by week, at the ad-set level. For each ad
set: week, reach, impressions, frequency, CTR, cost per
result.

Then pick out:
1. Ad sets where frequency has risen for three weeks running.
2. Of those, the ones where CTR is also dropping or cost per
   result is also rising — audience fatigue is likely there.
3. Ad sets where frequency is falling instead at the same
   spend — either the audience expanded or delivery got
   worse.

For each conclusion, say what numbers it's based on, and
separate what's visible in the data from what's your
hypothesis.

For ad account [account] and the period [the last 30 days],
compare performance by audience and targeting. List ad sets
by targeting type (custom audience, lookalike audience,
interest targeting, broad targeting, remarketing) along with
spend, results, cost per result, and reach. Group them by
targeting type and calculate a group total and average cost
per result.

Then answer:
- which targeting type is cheapest for us and which is most
  expensive
- where the difference is small enough that it could be
  chance
- which specific audiences are worth trying to expand and
  which I should consider turning off

For that last point, give a counterargument for each
suggestion too — why it might actually be a mistake.

Run a diagnostic on account [account]. I'm interested in
technical health, not performance.

1. Active errors and alerts on the account — what they are
   and what they concern.
2. Ads or ad sets that are rejected, in review, or not
   delivering, and why.
3. Campaigns with an active budget and zero impressions over
   the last [7] days.
4. Status of measured events: which conversion events are
   coming through, which aren't coming through at all, and
   which have a suspiciously low volume relative to clicks.
5. Changes to the account over the last [14] days from the
   activity log: who changed what, and when.

Rank the findings by how much each one is likely costing us,
and say whether each is a two-minute fix or a half-day one.

Check catalog [name] connected to account [account].

1. How many items it contains, how many are approved, and how
   many aren't showing up for some reason.
2. The most common rejection or error reasons, by item count.
3. Feed status: when it last updated, whether it updated
   cleanly, and how often it updates.
4. Items missing data needed for dynamic ads (image, price,
   availability, identifier).
5. Product sets and how many items each contains — find the
   empty or nearly empty ones.

At the end, give the three most urgent things to fix and an
estimate of how much of the catalog each one affects.

I'm about to make this change in account [account]: [description,
e.g. raise campaign X's daily budget from 500 to 1,500 Kč].

Don't do anything. Just answer:
1. What numbers is that change based on, and are they big
   enough to mean something?
2. What's the worst-case outcome — how much could I lose
   before I notice?
3. What should I watch after the change, and how long before
   it can be evaluated?
4. Is there a cheaper way to test the same hypothesis?
5. Three reasons this change might be a bad idea.

Take point 5 seriously, even if the change seems sound to you.

Pull a traffic-source breakdown from property [name] for the
period [July 1-31, 2026]. Table by channel (organic search,
direct, paid search, social, referral, email): users | new
sessions | engagement rate | key events | conversion rate

Then break the three biggest channels down into specific
sources and mediums. Finally:
- which channel brings the most people and which brings the
  most conversions (and whether it's the same one)
- how much traffic falls into unassigned categories and what
  that means for how much to trust the rest of the numbers

State the period and the property's time zone used.

For property [name] and the period [the last 30 days], do a
content review. Table by page, top 30 by pageviews: URL |
title | pageviews | users | average time | engagement rate |
key events

Then pick out and comment on:
1. Five pages with high traffic and low engagement — people
   arrive and leave right away.
2. Five pages with low traffic and high engagement — content
   that works, but nobody finds it.
3. Pages with decent traffic and engagement that still don't
   lead to any key events.

For each group, note what could be done about it, but keep
that separate from the description of the data.

Build a funnel report in property [name] for [the last 30
days] for this path:

Step 1: product page visit
Step 2: cart view
Step 3: checkout started
Step 4: order completed

For each step: number of users, how many continued on,
percent drop-off. Then:
- which step has the biggest drop-off in absolute numbers
- how the drop-off differs between mobile and desktop
- how the drop-off differs between new and returning visitors

Don't guess at causes of the drop-off from general e-commerce
knowledge. Write only what's visible in the data, plus a list
of questions I should verify elsewhere.

On [date] we published an article at [URL]. Evaluate it in
property [name].

1. Traffic to that page day by day from publication to today.
2. Where people are arriving from — broken down by channel,
   source, and medium, including how the mix has changed over
   time (first week versus most recent week).
3. Where they go from it — the most common next pages.
4. How many key events its visitors generated.
5. Compare it against the average of similar pages over their
   first [30] days.

Say whether traffic has settled or is still growing, and as
of what date you're basing that on.

For property [name], compare traffic quality by source over
[the last 90 days]. I'm not just interested in volume: for
every source and medium with at least [200] users, calculate
number of users, share of new users, pages per session,
session duration, engagement rate, and conversion rate to key
event [event name].

Sort by conversion rate and flag sources where it's high but
the volume is small enough that it might not mean anything.

Then name three sources worth investing more in, and for
each, a counterargument for why that might be a mistake.

Show me real-time data in property [name]: how many users are
on the site right now, which pages they're on, where they
came from, and what devices they're on.

Then compare whether that matches normal traffic for this
time of day, or whether something unusual is going on.

I have ad account [account] and Analytics property [property]
connected. For the period [July 1-31, 2026], build me one
combined view from both sides.

From the ad account, pull by campaign: spend, clicks, CPC,
results, cost per result. From Analytics, pull traffic from
paid sources, broken down so it can be matched to these
campaigns (by source, medium, and campaign).

Build a table where each campaign shows, side by side: clicks
from the ad account | sessions in Analytics | engagement rate
| pages per session | key events | conversion rate — and a
column with the percent difference between clicks and
sessions.

Then say which campaigns show the biggest mismatch, and
separate what's explained by the data from what's a
hypothesis. Don't try to smooth the differences out or
average them away.

Campaign [name]'s performance has dropped over the last [60]
days. Help me decide whether the problem is on the ad side or
the site side.

From the ad account, pull by week: spend, impressions, CPM,
clicks, CTR, frequency, results, cost per result. From
Analytics, pull by week for traffic from this campaign:
sessions, engagement rate, pages per session, conversion
rate.

Line these up on one timeline and answer:
- Is CTR falling while frequency rises? (audience fatigue)
- Is CPM rising at the same CTR? (a more expensive auction)
- Are clicks steady while engagement and conversion rate fall?
  (a problem past the click)
- Did anything change on the landing page around the time of
  the drop?

For each possibility, say whether the data supports it,
rules it out, or can't speak to it. Don't force a winner.

This is the template for my monthly report. Use it exactly
like this, add nothing and skip nothing.

Period: [August 1-31, 2026], compared against the previous
month.
Sources: ad account [account], Analytics property [property].

Section 1 - Summary: spend, results, cost per result,
sessions from paid sources, key events. Value, change, percent
change. Nothing more.
Section 2 - Campaigns: table sorted by spend, same columns
as last time.
Section 3 - Traffic: channels, top 10 landing pages.
Section 4 - What changed: five changes, one sentence each,
sorted by financial impact.
Section 5 - Needs a decision: three things that need my
decision, with supporting data.

Format: dollar amounts, percentages to one decimal place. No
introduction, no closing, and no advice I didn't ask for. For
every section, state the source and exact period of the data.