Can I use multiple MCP servers at the same time?

Yes. Claude Desktop and most MCP-compatible tools support multiple simultaneous MCP connections. You could use Ad Superpowers for ad platforms and a separate MCP server for your CRM or database.

Are open-source MCP servers safe to use?

Open-source servers are as safe as the code you review. Since they run locally, your data does not leave your machine. However, you are responsible for keeping them updated and ensuring credentials are stored securely. Always review the code before providing API keys.

How often do ad platform APIs change?

Frequently. Meta deprecates API versions roughly every 2 years and releases new versions every few months. Google Ads updates its API several times per year. This is why maintained MCP servers have an advantage — someone else handles the API updates for you.

Roundup

Best MCP Servers for Advertising in 2026

A practical comparison of MCP servers that connect your ad platforms to Claude, ChatGPT, and other AI tools.

10 min readAd SuperpowersUpdated 2026-02-11

MCP servers are the bridge between your AI tools and your advertising data. As adoption grows, the number of MCP servers targeting marketers is growing too. But they are not all the same — they differ in platform coverage, tool quality, security, and maintenance.

This guide compares the options available in 2026 for connecting advertising platforms to AI. We will look at what each option covers, where they differ, and how to decide which approach fits your workflow.

Fair disclosure: we build Ad Superpowers, one of the servers listed here. We will be upfront about what we do well and where other approaches might suit you better.

What to Look for in an Advertising MCP Server

Before comparing specific options, here are the criteria that matter:

Platform coverage: Does it connect to the ad platforms you actually use? Meta Ads and Google Ads are table stakes. LinkedIn, TikTok, GA4, and Google Search Console separate comprehensive solutions from basic ones.

Tool quality: Having a "Google Ads" tool is meaningless if it can only list campaigns. You need tools that support GAQL queries, audience breakdowns, creative analysis, and cross-date comparisons. The depth of each tool matters more than the count.

Authentication and security: How does the server handle your credentials? OAuth is the gold standard — you should never have to paste API tokens or share passwords. Encrypted token storage and read-only defaults are baseline requirements.

Maintenance: Ad platform APIs change constantly. Meta deprecated its v18 API last year. Google Ads is on v18. If the MCP server is not actively maintained, your tools will break when APIs update.

Multi-account support: Agencies and in-house teams often manage 5-50 ad accounts. The server should handle multiple accounts per platform without requiring separate connections for each.

Ease of setup: Some MCP servers require Docker, environment variables, and command-line configuration. Others work with a URL and an API key. For marketers, simpler is better.

Option 1: All-in-One MCP Server (Ad Superpowers)

The all-in-one approach connects multiple ad platforms through a single MCP server. Ad Superpowers is the main example of this approach.

What it covers: Meta Ads, Google Ads, Google Analytics 4, Google Search Console, LinkedIn Ads, and TikTok Ads — all through one connection URL. That is 29 tools across 6 platforms, including full campaign creation and management for Meta Ads and Google Ads.

How it works: You create an account, connect your ad platforms through OAuth (click and authorize), copy the MCP server URL, and paste it into Claude Desktop or your AI tool of choice. The whole setup takes about 2 minutes.

Strengths: Single connection for all platforms. Managed hosting (no Docker or servers to run). OAuth-based authentication with encrypted token storage. Pre-built workflows and skills that provide structured analysis templates. Regular updates as platform APIs change.

Considerations: It is a SaaS product, so the free tier has limits (3 ad accounts, 4 platforms). Pro plans start at €79/month. Because it is hosted, your API calls route through their server — though no data is stored.

Best for: Marketers and agencies who want the simplest path to connecting multiple ad platforms to AI. Works well for people who are not comfortable with Docker or command-line tools.

Option 2: Platform-Specific Open-Source Servers

The open-source community has built MCP servers for individual platforms. These are typically single-platform tools that you run locally.

What is available: There are open-source MCP servers for Meta Ads (via the Marketing API), Google Ads (via the API), and Google Analytics. Coverage for LinkedIn and TikTok is sparse.

How they work: You clone a GitHub repository, configure environment variables with your API credentials, and run the server locally (often with Docker or Python). Your AI tool connects to localhost.

Strengths: Free. Open source, so you can inspect and modify the code. Data stays entirely on your machine. No third-party server involved.

Considerations: You need one server per platform, which means multiple connections to manage. Setup requires technical knowledge (API keys, Docker, Python environments). You are responsible for maintenance when APIs change. Token refresh, error handling, and rate limiting vary in quality. No multi-account management built in.

Best for: Developers and technical marketers who want full control and are comfortable with command-line tools. Good for single-platform use cases where you only need Meta or Google Ads.

Option 3: Build Your Own

If you have engineering resources, you can build a custom MCP server tailored to your exact needs using the MCP SDK (available in Python and TypeScript).

How it works: You use the MCP SDK to create a server that exposes tools for the specific API calls you need. You handle authentication, data formatting, and error handling yourself.

Strengths: Complete customization. You can expose exactly the tools your team needs, with the exact parameters and response formats you want. You can integrate internal data sources alongside ad platforms.

Considerations: Significant engineering investment. You need to understand both the MCP protocol and each ad platform's API. Ongoing maintenance as APIs change. Authentication and security are your responsibility.

Best for: Teams with dedicated engineering resources who need custom integrations or who want to combine ad platform data with internal systems (CRM, data warehouse, etc.).

Option 4: General-Purpose Data Connectors

Some tools approach this from the data integration side. Platforms like n8n, Make, and Zapier are adding AI and MCP-adjacent capabilities that can bridge ad platforms and AI tools.

How they work: You set up workflows that pull data from ad platforms and feed it into AI tools, often through intermediate steps like Google Sheets or webhooks.

Strengths: Flexible. You can combine ad data with CRM, email, and other business data. Workflow automation capabilities beyond just querying. Large connector libraries.

Considerations: Not native MCP — the AI tool does not directly query your ad platforms. There is usually a lag (data is pulled on a schedule, not on demand). More complex setup. Costs add up with multiple connectors and workflow runs.

Best for: Teams that already use workflow automation tools and want to add AI capabilities to existing data pipelines. Better for scheduled reporting than ad hoc analysis.

Head-to-Head Comparison

Here is how the options compare on the criteria that matter:

Platform coverage — All-in-One: 6 platforms (Meta, Google Ads, GA4, GSC, LinkedIn, TikTok). Open-Source: 2-3 platforms (Meta, Google Ads, GA4). Custom: Whatever you build. Data Connectors: Varies widely.

Setup time — All-in-One: 2 minutes. Open-Source: 30-60 minutes per platform. Custom: Days to weeks. Data Connectors: 1-2 hours.

Technical skill needed — All-in-One: None. Open-Source: Moderate (Docker, CLI). Custom: High (API development). Data Connectors: Low-Moderate.

Ongoing maintenance — All-in-One: Managed for you. Open-Source: You handle API changes. Custom: You handle everything. Data Connectors: Moderate.

Cost — All-in-One: Free tier available, Pro from €79/mo. Open-Source: Free (plus your time). Custom: Engineering time. Data Connectors: Platform subscription + per-task pricing.

Security — All-in-One: OAuth, encrypted tokens, no data storage. Open-Source: Local only, you manage credentials. Custom: Your implementation. Data Connectors: Platform-dependent.

Which Option Is Right for You?

The right choice depends on your team and workflow:

If you are a solo marketer or small agency: Start with an all-in-one solution. You want the fastest path to value without technical overhead. The free tier lets you test the experience before committing.

If you are a developer or growth engineer: Try open-source servers first. You will appreciate the control and can customize to your needs. Supplement with an all-in-one server for platforms not covered by open source.

If you have a dedicated data team: Consider building custom or combining approaches. A custom MCP server that wraps your data warehouse alongside ad platforms gives you the most powerful analysis capability.

If you already use workflow automation: Extend your existing n8n or Make setup to feed data into AI tools. This leverages your current infrastructure.

The landscape is evolving quickly. New MCP servers appear every month, and existing ones add platforms and tools regularly. Whatever you choose today, the standard protocol means you can switch or add servers without changing your AI tool.