10 MCP Prompts for Unified Marketing Data in Claude

Your GA4 dashboard shows a 20% traffic dip. Meta Ads reports a 15% higher CPA. Meanwhile, Perplexity and ChatGPT now surface your competitor’s blog for your best non-brand terms. You’re toggling six tabs, copying numbers into a spreadsheet, and still missing the full picture without unified marketing data. No single tool gives you an answer, because the data lives in separate silos.

The fix: bring the data directly into your reasoning environment. With Claude’s Model Context Protocol (MCP) support, you can connect live data sources, Google Analytics, Meta Ads, HubSpot, even your first-party AI visibility feeds from SearchFit, directly to the chat. Instead of you jumping between tools, Claude queries the relevant APIs on your behalf and builds the analysis right there.

This guide gives you 10 ready-to-run MCP prompts designed to unite your marketing stack inside Claude. Each prompt targets a specific cross‑channel problem: spotting gaps between paid and AI visibility, auditing content for answer‑engine performance, building quick‑turn executive summaries, and more. We’ll cover:

• Exactly what you need set up before you start (MCP‑enabled Claude, SearchFit’s MCP server, and your own data connectors)
• The exact text of each prompt, with placeholders for your own dates, campaigns, and domains
• How to interpret the output and when to press for deeper answers
• Pro tips that prevent you from wasting tokens on flaky data or generic responses

Let’s stop the tab‑switching. Here are the 10 prompts that turn Claude into your unified marketing command center.

Prerequisites

You’ll need a Claude Pro, Team, or Enterprise account with MCP support enabled. The full Model Context Protocol documentation explains how MCP creates a secure, standard way for Claude to talk to external tools. In short, it’s a client-server protocol: Claude is the client, and each data source (your CRM, your analytics, your AI visibility tracker) runs a small MCP server that Claude can call during a conversation.

For these prompts, you’ll want at least these MCP servers connected:

1. SearchFit MCP server: provides live AI visibility data across ChatGPT, Perplexity, Gemini, Claude, and Grok, including citation tracking, SOV metrics, and content audit scores. Get started with the setup in our 10 Questions You Can Ask the SearchFit MCP Server guide.
2. Google Analytics MCP server: surfacing real‑time metrics, audience behavior, and conversion data. Many teams build their own using the Google Analytics Data API or use existing community connectors.
3. Meta Ads MCP server: for cost, impression, and conversion data across Facebook and Instagram campaigns. Build yours with the Meta Marketing API.
4. HubSpot MCP server: pulling CRM lifecycle stages, lead scores, and email performance. You can wire it directly with the HubSpot API.

Many additional connectors are available, from Microsoft Advertising to Notion. The official Notion MCP guide demonstrates how straightforward the process can be, and the same pattern applies to the connectors Claude supports. Anthropic’s own introduction to the Model Context Protocol explains why this approach is superior to copy‑pasting spreadsheets.

Pro tip: Always install the SearchFit MCP server first. Without AI‑visibility benchmarks, your analysis misses the largest shift in organic discovery: the answer engines where your customers are already asking questions. The other servers layer in channel‑specific metrics.

Once everything is plugged in, open a new Claude conversation, confirm the MCP tools appear in the connection list, and you’re ready.

1. Cross‑Channel Performance Snapshot

Prompt:

Pull the following data for the last 7 days (compared to the previous 7-day period):
- From Google Analytics: total sessions, bounce rate, conversion rate, and revenue for the /blog and /product pages
- From Meta Ads: spend, impressions, clicks, and CPA across all active campaigns
- From SearchFit: top 5 AI‑cited URLs across ChatGPT, Perplexity, and Gemini, with week‑over‑week change in citation count
Present the results in a single table, then write a 3‑bullet executive summary highlighting the biggest shifts and whether AI citations correlate with traffic changes.

Why it works: Most teams check each tool in isolation. This prompt forces a unified view, making it immediately obvious if, say, a spike in AI citations for a product page occurred alongside a dip in organic sessions, a sign that answer engines are absorbing click volume.

Pro tip: Set the date range to your typical reporting cycle (weekly, bi‑weekly). If you run this on Monday mornings, Claude effectively becomes your automated weekly digest. Bookmark the conversation and replay it each week by only updating the date.

Warning: If any MCP server returns missing or empty data, often GA4 due to data‑sharing settings, ask Claude to flag the specific tool and recommend a fix instead of proceeding with partial numbers.

2. AI Visibility vs. Paid Search Gap Analysis

Prompt:

From SearchFit, grab the 20 keywords where we have the highest combined AI visibility (ChatGPT + Perplexity + Gemini). For those same 20 keywords, pull the previous 30‑day Google Ads data: impressions, clicks, and cost.
Identify keywords where:
1. We have an AI visibility share > 5% but zero paid presence.
2. We spend > $500/month in paid but the AI share is < 2%.
For each identified keyword, suggest an action: launch a new campaign, build an AI‑optimized landing page, or both.

Why it works: This reveals true missed opportunity. AI answer engines often cite content that doesn’t rank well in traditional search. If you’re paying for traffic on keywords where AI is also your content, you might be double‑spending or under‑funding the emerging channel. The output is a clear investment grid.

Pro tip: Adjust the visibility threshold based on your market. For niche B2B SaaS, a 5% AI share is significant; for e‑commerce, you might need 10%. Ask Claude to explain the rationale behind each action suggestion so you can override decisions when context demands.

Warning: Paid data from Google Ads requires a properly authenticated MCP server. If you’re using a community connector, verify it pulls historical data accurately. Some connectors only return aggregated stats, missing day‑part trends. Validate by spot‑checking against the native Ads dashboard.

3. Attribution Across Organic, Paid, and AI Sources

Prompt:

Using Google Analytics session source/medium data, Meta Ads campaign performance, and SearchFit AI citation data:
For the last 30 days, attribute total tracked conversions across three buckets: Organic (non‑paid search), Paid (search + social), and AI‑Assisted (any user who had an AI citation in the last 7 days before converting).
Output a pie chart description (percentages and absolute numbers) and a short narrative on how the AI‑Assisted segment is growing relative to Paid.

Why it works: Multi‑touch attribution is messy. By pulling AI citation data into the same context, you can approximate the influence answer engines are having, even if you can’t directly tie a click to a conversion from ChatGPT. The prompt uses a 7‑day lookback window to capture those who researched through an AI tool before visiting your site.

Pro tip: For a more advanced version, ask Claude to segment further by AI engine (ChatGPT vs. Perplexity vs. Gemini). This can reveal differences in intent; for example, Gemini might drive more top‑of‑funnel exploration while Perplexity drives transactional queries. That insight is gold for content strategy.

Warning: If your MCP tool returns zero AI‑assisted conversions, it likely means no user was flagged with a recent AI citation. This doesn’t mean AI had no impact, it means the tracking window is tight. Extend to 14 or 21 days and re‑run.

4. Content Audit with AI and Engagement Signals

Prompt:

From SearchFit, fetch all URLs on our domain that have been cited by ChatGPT, Perplexity, or Gemini in the last 60 days, along with their citation count and the top 3 sources that out‑rank us for each URL. From Google Analytics, pull average time on page, scroll depth (if available), and conversion rate for the same URLs.
List the top 10 under‑performing pages (high AI citation count but low engagement) and suggest 3 specific on‑page changes to improve dwell time and conversion.

Why it works: A citation without engagement is a missed lead. This prompt uncovers pages that AI is promoting but real users bounce from, so you can prioritize quick fixes, better headers, stronger CTAs, or improved page speed, that convert AI traffic into leads.

Pro tip: Have Claude prioritize suggestions that also strengthen the page’s E‑E‑A‑T signals, which are essential for maintaining AI visibility over time. If you need help building the audit workflow into an agentic process, check out Building Custom AEO Workflows With the SearchFit API.

Warning: Scroll depth is not universally available via standard GA4 APIs. If your connector doesn’t support it, replace that metric with time on page or pages per session. Don’t run the prompt with missing data; the suggestions will be surface‑level.

5. Ad Spend Efficiency with AI Share of Voice Overlay

Prompt:

For our top 30 product categories (as defined in our CMS hierarchy), pull:
- Meta Ads spend, clicks, and ROAS over the last 30 days
- SearchFit AI Share of Voice (SOV) across ChatGPT, Perplexity, and Gemini for each category
Create a scatterplot description: X‑axis is Ad Spend, Y‑axis is AI SOV. Highlight outliers where:
- High spend, low AI SOV → ad‑dependent categories
- Low spend, high AI SOV → organically strong categories
Suggest whether to reallocate budget or build supporting AI‑optimized content.

Why it works: Marketing budgets often lag behind visibility shifts. This prompt puts your ad spending in direct context with free AI visibility, so you can make data‑backed arguments to shift dollars from over‑funded paid categories to categories where a small content investment could capture significant AI citation share.

Pro tip: Use the scatterplot description (Claude will give you detailed data points, not an actual image) to build a quick visualization in your BI tool. The narrative it generates is immediately shareable with leadership. And for guidance on structuring AEO dashboards that combine GA4, GSC, and AI SOV data, see Multi‑Source Analytics Dashboards in SearchFit.

Warning: AI SOV from SearchFit reflects what the engines actually cite, not a broad keyword universe. Ensure the product categories you feed match the topics SearchFit is tracking. If you’re unsure, use SearchFit’s 10 Prompts to Try With the SearchFit Claude Plugin article to verify your tracking setup.

6. Funnel Leak Detection Using Unified Data

Prompt:

Join our HubSpot CRM lifecycle stage progression data with Google Analytics session sources and SearchFit’s AI citation corpus:
Identify all contacts who reached Opportunity stage in the last 90 days, then trace back to their first‑touch source.
Output a table showing the proportion of opportunities that originated from:
- Organic search (Google)
- Paid search and social
- Direct
- Any contact who was served an AI citation within 7 days before their first visit
Highlight if any drop‑off is occurring between MQL and SQL for AI‑sourced opportunities and suggest a nurture sequence tweak.

Why it works: AI‑sourced leads often behave differently, they’ve done deep research before they ever fill a form. This prompt reveals if your current nurture paths are losing them, giving you data to tailor sequences specifically for answer‑engine‑influenced buyers.

Pro tip: If HubSpot’s lifecycle isn’t granular enough, use custom properties. The prompt can be extended to pull lead score history and identify which scores predict AI‑sourced opportunity creation. That’s the kind of model you’d otherwise need a data scientist to build.

Warning: The 7‑day attribution window is an assumption. Validate by running the analysis with a 3‑day and 14‑day window and comparing the results. The one with the strongest correlation to opportunity creation is your best proxy.

7. Campaign Briefing with Real‑Time AI Visibility

Prompt:

I’m preparing a campaign brief for [campaign name]. The product is [short description] and the target audience is [persona].
Pull from SearchFit: the current top‑cited AI content for the main problem this product solves, across all tracked engines. From Meta Ads: the top‑performing ad creatives from similar campaigns in the last 90 days. From Google Analytics: the landing pages that convert highest for this audience segment.
Generate a 1‑page creative brief that includes: the AI conversation landscape (how the problem is being answered today), our biggest AI visibility gap, ad copy angles that leverage what’s already working, and the highest‑converting landing page to pair with the campaign.

Why it works: Most briefs ignore answer engines entirely. This forces the creative team to ground their work in how AI is already shaping the conversation, ensuring your ads, landing pages, and GAI‑optimized content speak the same language as the AI models your audience is using.

Pro tip: After you get the brief, feed it back to Claude with a follow‑up to draft actual ad copy and an AI‑optimized blog outline. You’ll move from insight to production in a single session. The Building Your AEO Workflow Inside Claude guide shows you exactly how to chain such tasks.

Warning: Make sure your SearchFit tracking includes the engines where your persona spends time. A B2B marketing leader might favor Perplexity over ChatGPT; an e‑commerce shopper leans on Gemini. If you’re unsure, use SearchFit’s platform to run a quick audience‑engine overlap report.

8. Competitor AI Share of Voice vs. Your Paid Spend

Prompt:

From SearchFit, pull the AI Share of Voice for our brand vs. [list 3 competitors] for the topic cluster [topic cluster name]. From Google Ads and Meta Ads, pull total spend for our campaigns targeting the same topic cluster.
Create a comparative table: each brand’s AI SOV, our ad spend, and a ratio of AI SOV per $10k spent. Return the competitor rankings and highlight if any competitor achieves a higher AI SOV with lower estimated spend (suggesting superior organic content). Recommend a content‑led strategy to match their AI presence without increasing ad budget.

Why it works: The AI visibility landscape is often the inverse of the paid landscape. A competitor might have no ad budget but dominate AI citations because their content is deeply referenced by the models. This prompt reveals if you can outmaneuver them by shifting from paid to content. The “AI SOV per $10k” metric makes the trade‑off crystal clear to your CFO.

Pro tip: Pair this prompt with a follow‑up that uses SearchFit to analyze the technical structure of the top competitor’s content, heading density, semantic entities, trust signals. SearchFit’s Inside the SearchFit Agent Team: Researcher, Writer, Validator, Scorer breaks down exactly which signals the AI models reward.

Warning: AI SOV is not directly comparable to traditional share of search. It measures citation frequency in answer engines, not query volume. Don’t represent it as a 1:1 to organic ranking. Use it as a directional signal for authority.

9. SEO + AEO Content Prioritization

Prompt:

From Google Search Console, pull the top 20 queries driving impressions but having a CTR below 2%. From SearchFit, pull the AI‑cited content for those exact queries.
For each query, compare the type of content that ranks in Google vs. what AI engines cite (article vs. landing page vs. product page). List queries where AI engines primarily cite a different content type than what ranks in Google, and recommend a new piece of content that would satisfy both.

Why it works: This is the clearest operating signal for AEO. When the answer engine and the traditional SERP show conflicting content formats, you have a chance to create a single asset that wins in both, something few teams do because they don’t look at the data side by side.

Pro tip: Extend the prompt by asking Claude to outline that new piece of content, then feed it to SearchFit’s content generation tool or use it as a brief for your writers. Check out From Prompts to Agents: The Evolution of SearchFit's Content Engine to see how agentic systems handle this at scale.

Warning: GSC data can be delayed. For real‑time prioritization, use the last 28 days and ask Claude to note if any sudden changes appear that might indicate an algorithm update. If so, flag them instead of acting on stale data.

10. Weekly Executive Summary with AI Citation Alerts

Prompt:

Using all connected sources, generate a weekly executive summary covering:
- Top 3 metrics from GA4 (sessions, conversion rate, revenue) with week‑over‑week change
- Top 3 performing ad campaigns (Meta + Google) by ROAS
- Top 5 AI‑cited pages (SearchFit) with change in citation count
- Any new AI engine where we gained our first citation
- Any competitor that entered our tracked AI SOV for the first time
- One recommended action for the marketing team this week based on the data
Format the summary as a concise email body and a 3‑slide presentation outline.

Why it works: It removes the “update by committee” meeting. Everyone gets the same facts from the same sources, and the recommended action, coming from the data, not someone’s gut, is defensible. The email body and slide outline are immediately ready for Monday stand‑up.

Pro tip: Schedule Claude to run this on a Monday morning by chaining it through your task automation tool (e.g., Zapier webhook that triggers the MCP prompt). You can even have it post the summary to a Slack channel. Our guide How Marketing Teams Use SearchFit Inside Claude walks through team‑wide integration patterns.

Warning: If a new competitor appears for the first time, treat it as a high‑priority alert. AI visibility tends to compound; once a competitor starts being cited, it’s hard to dislodge them. Have Claude immediately run a deep‑analysis prompt on that competitor using SearchFit’s data.

Conclusion and Key Takeaways

Unified marketing data isn’t just about convenience. It’s the difference between optimizing for yesterday’s channels and capturing the traffic that moves through answer engines right now. These 10 MCP prompts give you a repeatable system to:

• Measure AI’s real impact on your funnel, not just guess with proxy metrics. Use prompts 1, 3, and 6 to quantify how answer‑engine visibility feeds leads and revenue.
• Diagnose wasted budget by placing paid spend next to AI share of voice. Prompts 2, 5, and 8 reveal immediate re‑allocation opportunities.
• Prioritize content creation with real‑time AI citation data, not months‑old keyword research. Prompts 4, 7, and 9 turn content strategy into a weekly play, not a quarterly project.
• Automate internal reporting so your team spends more time acting on insights than gathering them. Prompt 10 and the snapshots from prompt 1 create a dashboard‑free review cadence.

As you build these prompts into your workflow, remember three habits:

1. Always validate the data, spot‑check numbers against native tools for the first few runs until you trust your connectors.
2. Don’t run prompts on auto‑pilot. The real value comes from the follow‑up questions you ask Claude once it surfaces an anomaly. Use the insights to dig deeper.
3. Treat your MCP server setup like a product infrastructure investment. As new data sources become available, add them. A well‑connected Claude instance becomes a team‑level analytics resource, not just an individual tool.

If you’re interested in embedding these prompts directly into Claude via the SearchFit plugin, start with 10 Prompts to Try With the SearchFit Claude Plugin. For teams that need a dedicated AEO agent, our guide on Building AEO Agents on Top of SearchFit MCP shows you how to go from manual prompts to fully agentic workflows.

Data silos are a choice, not a necessity. The protocol exists. The prompts are here. The only step left is to open Claude and connect.