Run1Ads: live Meta ads consultant MCP Server

Add a fix to the brand's Run1Ads fix list. Pass item_key from get_today to add that finding with its steps and the number to track, or pass your own title. Writes to Run1Ads only. Nothing changes in Meta or on your store.

Use for "what have I committed to?" or "did my fix work?". Returns the fixes the user chose to do, filtered by status (open, done or all, default all), each with its fix_id and, once newer data is saved, whether the tracked number moved after the fix was marked done. Use the fix_id with mark_fix_done. For new findings use get_today.

Use for questions about the store and tracking, not the ads: "is my Pixel working?", "does my store work in the Instagram browser?", "why do visitors from my ads leave?". Returns whether the store works inside the Instagram and Facebook app browser, whether Add to Cart shows on mobile, whether the Meta Pixel fired, five store scores out of 100, the weakest product pages, and the "Problems to fix" for visitors from Meta ads (landing pages they leave straight away, app layers or buttons that ignore taps, store script errors, weak campaigns, slow pages), each with the numbers, why it matters, fix steps and example visits, plus where they leave. Also the top 3 causes of "Why visitors are not buying" (all of them: get_why_no_buy). For funnel rates use get_sales_funnel; for each campaign's visit to cart funnel use get_campaign_funnels. Saved data from the latest daily store walk and daily visitor review.

Use for "what should I fix on my website?" or "what on my store is costing me sales?". Returns the problems on the store that cost sales from every campaign, each listed once and ranked by impact, with what we saw, the fix, how long it takes and who does it, and what it could be worth in dollars. Found from visits from the brand's Meta ads, no recordings kept. Same view as Store fixes in the Run1Ads app.

Ask the Run1Ads consultant a question about this brand in plain English. It answers from the saved account data only, with exact steps you can apply. It advises only and never changes anything. Up to 10 questions per brand per day, shared with the app.

Use for "who is buying?", "which placement works best?" or "is my custom audience big enough?". Returns buyers in the period, saved custom audiences with size and whether each is big enough to deliver, and results split by placement (Instagram, Facebook, Reels, Stories). For overall ROAS use get_performance; for funnel drop offs use get_sales_funnel. Saved data from the latest Meta read.

List the brands this Run1Ads account can open, with each brand id, name and whether Meta is connected. Use it first when the user manages more than one brand or names a brand, then pass brand_id to the other tools. Every other tool uses the most recently updated brand when brand_id is left out.

Use for "which campaign sends the best visitors?", "which landing page is weakest?" or "where do visitors from each campaign drop out?". Returns, per campaign (utm_campaign or campaign id, plus visits not tagged with a campaign) for the last 7 and 30 days: visits, the share who left straight away (one page, no scroll past the first quarter), who viewed a product, reached the cart and reached checkout (or "not visible" when the checkout runs on a separate Shopify checkout page), pages per visit, the top landing page, the weakest step, and which campaign is best and weakest. Same table as the app's Funnel by campaign. For Meta's own funnel use get_sales_funnel. Saved data from the daily visitor review.

Use for "how are my campaigns doing?", "which campaign should I fix or give more budget?" or "where does each campaign lose buyers?". Returns each campaign (biggest spend first) with its share of budget, spend and ROAS, then every step from ad click to sale (clicked the ad, looked at a product, added to cart, went to checkout, sales) against similar stores, the one step where it loses the most buyers, what to do, the problem, the fix and what it could be worth in dollars. Same view as Campaigns in the Run1Ads app. Explain ROAS in plain words when the user may not know it.

Use for "what are my competitors doing?". Returns recent changes in the brand's chosen competitors' Meta ads as conclusions: what changed, what it means for this brand and what to do (or that nothing is needed). We study their ads so the user does not have to; competitor ads themselves are not shown. Competitors are set in the Run1Ads app.

Use for "is my ad copy good?" or "why do viewers skip my video?". Returns a review of ads already running: first line, headline, call to action and link, with a score and specific issues, plus video ads from the last 7 days with hook rate, hold rate, where viewers drop off, a visual review of the frames and fix steps. For which ads are tired use get_ad_fatigue. Saved data from the latest review.

Use for "which ads are getting tired?" or "should I refresh my creative?". Returns each ad's frequency, click rate and its change, and hook rate, highest frequency first, so you can see which ads people have seen too often. For the copy and video content itself use get_ad_copy_review; for account totals use get_performance. Saved data, refreshed by the hourly check.

Use for "is anything broken right now?" or "any alerts?". Returns when the hourly live check last ran, today so far, open alerts with critical first (for example spend with no sales, a rejected ad, a broken Pixel or store) and, unless include_resolved is false, alerts resolved in the last 7 days. Each alert says why it matters and how to fix it. get_today shows only the top items; this shows them all. Saved data, checked every hour.

Mark a fix on the fix list as done. Run1Ads records the number at that moment so it can show whether the fix worked once newer data is saved. Writes to Run1Ads only.

Use for questions about results and goals: "what's my ROAS?", "how much did I spend?", "am I on track this month?". Returns saved Meta ads totals (spend, revenue, ROAS, purchases, click rate, cost per click, frequency) and this month's ROAS goal with the gap to it. For where buyers drop out use get_sales_funnel; for per ad tiredness use get_ad_fatigue. Saved data, refreshed by the hourly check.

Use for "where am I losing buyers?" or "people click but don't buy". Returns each step from ad click to purchase as how many in 1,000 move on, the e-commerce benchmark per 1,000, above or below, the trend, and which step loses the most buyers. For totals like spend and ROAS use get_performance; for why visitors leave the store use get_store_checks. Saved data, refreshed by the hourly check.

Send one finding from get_today to the brand's own Meta ad account as a PAUSED campaign and ad set, so you can finish and switch it on yourself in Ads Manager. Nothing spends. Only findings marked draftable can be sent. Only use this when the user has clearly asked for it, and pass confirm true. Without confirm it returns a preview and sends nothing.

Start here for "what should I do today?" or "what's wrong with my Meta ads?". Returns today's brief: the one fix to do first, other findings ranked by buyers lost (each with its item_key, the number behind it and exact steps), what changed, what is working and the live check status. It summarises the other tools; use get_performance for full numbers, get_live_alerts for every alert, get_sales_funnel for each funnel step. Also returns the Today screen: needs_you_now (critical live alerts, shown first), the headline (for example "3 fixes could lift your ROAS from 1.9x to about 2.6x", all estimates) and up to 3 fixes with problem, fix and worth. Pass an item_key to add_to_fix_list or send_paused_draft. Saved data from the latest hourly check.

Use for "why are visitors from my Meta ads not adding to cart, not checking out or not buying?". Returns the stage strip from visit to product, add to cart, checkout and purchase (with counts, or "not visible" when we cannot see that step yet), the biggest drop against the e-commerce benchmark, and the likely causes ranked by buyers lost (about), each with its stage, how likely it is (high, medium, low), what we saw with the numbers, the likely cause, 2 to 4 fix steps and what would confirm it. Covers traffic and targeting, ad to page promise, technical problems (in app browser, speed, pop ups, failed adds, sold out products, the Pixel), the product page, cart and checkout friction, measurement gaps and the Meta account. Also lists what we cannot see yet and how to see it (for example the optional Shopify checkout pixel). Same as the app's "Why visitors are not buying". Saved data from the daily visitor review, computed on demand when missing.