Introducing adextract MCP server: connect your ad intelligence to any AI workspace

We are shipping the adextract MCP server today. It connects our competitive ad intelligence platform directly to your AI workspace so you can pull live competitor data, analyze ad creatives, and monitor market shifts without switching tabs or exporting CSVs.

The Model Context Protocol (MCP) is an open standard created by Anthropic in late 2024. It has grown to over 10,000 servers by early 2026, but marketing-specific MCP servers are still rare. Most ad platforms do not expose MCP endpoints yet. adextract is one of the first competitive ad intelligence tools to ship a production MCP server.

This is a product update about what we built, why we built it, and how you can start using it today.

What the adextract MCP server is

An MCP server is a bridge between an AI agent and a data source. It handles authentication, query execution, and response formatting so you can ask questions in plain English and get structured answers from live data.

The adextract MCP server exposes our competitive ad intelligence engine as a set of tools your AI can call. Instead of logging into adextract, running a search, and copying results into a chat window, you ask Claude or ChatGPT a question and the MCP server fetches the answer from live competitor data.

Here is what the server exposes:

Competitor ad search across Google, Meta, TikTok, and LinkedIn. Creative library queries by industry, format, or messaging angle. Ad spend estimates and impression volume for any competitor. Trend detection that flags when a competitor shifts strategy or launches new creative.

The server works with any MCP-compatible client: Claude Desktop, Claude Code, ChatGPT, Gemini, Cursor, Windsurf, VS Code, and any other tool that supports the protocol. You connect once, and every AI workspace you use gets access.

Why we built it

Performance marketers and ad agencies spend too much time on context switching. You research competitors in one tool, analyze data in a spreadsheet, and discuss findings in an AI chat. Each handoff introduces friction and makes it harder to act on insights.

We built the MCP server to collapse that pipeline. You stay in your AI workspace the whole time. The bot becomes your competitive intelligence analyst, pulling fresh data from adextract whenever you ask.

Three specific problems drove this build:

First, manual data export is the default in competitive ad intelligence. Most teams export CSVs from ad spy tools, clean them in Excel, and paste findings into a chat. That loop breaks the flow of analysis. The MCP server removes the export step entirely.

Second, AI agents are becoming the primary interface for marketing work. Teams already use Claude and ChatGPT for campaign analysis, creative briefs, and strategy documents. But the AI cannot see your adextract data without the MCP bridge. We wanted to meet teams where they already work.

Third, the MCP ecosystem now has over 10,000 servers but almost none are marketing-specific. Google Ads MCP and GA4 MCP exist, but they are read-only and platform-specific. No competitive intelligence tool had shipped a production MCP server. We saw the gap and decided to fill it.

How it works with any AI workspace

The architecture is straightforward. You connect your adextract account once through an OAuth flow. The MCP server authenticates with our API, caches your credentials, and exposes a set of tools your AI client can discover and call.

When you ask Claude a question like "show me the top five competitors running video ads in the DTC skincare space this month," here is what happens:

Claude identifies that this requires competitor ad data. It calls the search_competitor_ads tool exposed by the adextract MCP server. The server authenticates with your adextract account, queries our ad intelligence engine for DTC skincare competitors running video ads, and returns structured results. Claude formats the data into a readable table and presents it.

The server handles authentication tokens, rate limits, query execution, and error handling. If the API is temporarily unavailable, the server returns a clear error message instead of a cryptic stack trace.

One architecture decision worth calling out: the MCP server is read-only by design. It pulls data from adextract and returns it to your AI. It does not modify campaigns, change budgets, or write back to ad platforms. We believe competitive intelligence tools should inform decisions, not execute them automatically. You stay in control.

Real workflows you can run today

Here are four workflows that work today with the adextract MCP server connected to your AI workspace:

Competitive landscape monitoring. Ask your AI to pull a weekly report on what ads your top three competitors are running, which channels they are active on, and whether their creative approach has shifted. Instead of manually checking each platform, you get a structured briefing in one prompt.

Creative analysis and inspiration. Upload a competitor ad screenshot or describe their angle, and ask your AI to find similar creatives in the adextract database. The MCP server queries by industry, format, and messaging pattern. You see what is working across your category without guessing.

Anomaly detection. Ask your AI to flag when a competitor suddenly increases ad volume, launches a new product line, or changes their pricing messaging. The MCP server compares current data against historical baselines and surfaces the shift. You catch competitive moves early.

Campaign planning with live competitor context. Before launching a new campaign, ask your AI to research how competitors in your space are positioning similar products. The MCP server returns active ad examples, estimated spend ranges, and messaging themes. Your brief is grounded in real market data, not assumptions.

These workflows work with Claude, ChatGPT, Gemini, and any other MCP-compatible client. If you use AI agents for competitor ad research, the MCP server feeds them real-time data instead of stale exports. And if you run Google Ads competitor analysis, the server pulls cross-platform competitive context alongside your Google-specific data.

Getting started in three steps

Setup takes under five minutes:

First, log into your adextract account and navigate to Settings. Find the MCP Server tab and click Enable. You will get a unique server URL and an API key.

Second, add the MCP server to your AI client. For Claude Desktop, open Settings, go to the MCP Servers section, and paste the URL and API key. For ChatGPT and Gemini, the process is similar. Full setup guides are in our docs.

Third, start asking questions. Try "show me the top DTC brands running video ads this week" or "compare the ad strategies of my top three competitors." The MCP server pulls live data from adextract and returns structured results your AI can format and explain.

The MCP server is available on all adextract plans starting today. If you are on the free tier, you get access to basic competitor search and creative queries. Paid plans unlock trend detection, spend estimation, and higher query limits.

What comes next

This is version one. We are shipping the core competitor search and creative analysis tools first because those are the workflows our users run most often. Here is what is coming:

Scheduled reports. Configure your AI to pull a competitive briefing every Monday morning and post it to Slack or email. The MCP server handles the data, and Slack MCP or Gmail MCP handles the delivery.

Cross-platform ad spend estimates. We are building models that estimate competitor spend across Google, Meta, TikTok, and LinkedIn simultaneously. The MCP server will expose those estimates as structured data your AI can query directly.

Agentic workflows. The current server is read-only by design. But we are exploring approval-gated write paths: your AI analyzes competitor data, drafts a campaign brief, and presents it for your approval. You stay in the loop, but the busywork disappears.

More MCP-compatible clients. We test against Claude Desktop, Claude Code, ChatGPT, and Gemini today. We are adding Cursor, Windsurf, and VS Code support based on user requests. If your preferred AI workspace is not on the list, tell us and we will prioritize it.

We built this because competitive ad intelligence should live where you do your thinking. For most performance marketers and agency teams in 2026, that place is an AI workspace. The MCP server removes the last mile between your data and your decisions.

Enable it in your adextract settings and try it today. If you run into issues or have feature requests, the MCP server is a work in progress and we want your feedback.

Frequently asked questions