How far will Contentful MCP take your intelligent content operations?

Spoiler alert: As far as your imagination and creativity will take you.

Based on early indications, the Model Context Protocol (or MCP) is reshaping how teams build and manage digital experiences. From content creation to orchestration across systems, the shift toward agentic workflows — where AI agents act on behalf of humans — is moving fast.

To support this movement, the industry has been uniting around MCP, a standard that allows large-language-model (LLM)-based agents to interact with business systems in a predictable, structured way. Earlier this year, we introduced the Contentful MCP server and published an article explaining MCP in general. If you’re new to MCP and want to learn more, we recommend starting with this first post.

This post takes a deeper dive into our own MCP journey at Contentful: why we built and open-sourced our MCP server, what we’ve learned so far, and where we think it might go next.

What the Contentful MCP server does

At its core, the Contentful MCP server acts as a bridge between AI agents and Contentful workspaces. Instead of writing custom integrations for every LLM or assistant, MCP standardizes the way these systems connect.

With the server in place, AI can, among other tasks:

• Manage entries and assets (create, update, publish, unpublish).

• Explore and update content types.

• Automate operational tasks like tagging, environment management, and migrations.

This means agents can perform meaningful actions — like creating content, running bulk updates, or helping with SEO checks — using natural language prompts.

Why we built and open-sourced the Contentful MCP server

We released the Contentful MCP server as open source because we believe the future of AI in content operations should be transparent, customizable, and interoperable. 

By open-sourcing, we give customers the freedom to audit and customize the server to fit their unique needs, contribute improvements that benefit the entire community, and develop confidence in a process where the code is fully visible and governed by open standards.

As MCP gains momentum across the industry, this approach keeps Contentful aligned with emerging standards while helping customers prepare for what’s next.

What we’ve learned so far

Since launching the server, developers have started experimenting with it in local environments, and companies in sectors like media, retail, and tech are testing how MCP can streamline repetitive tasks and accelerate migrations.

A few takeaways:

• Developer / product manager curiosity is very high. Many want to see how far they can push MCP with complex workflows.

• Operational teams value automation. MCP + AI is especially good at handling the mechanical, repetitive tasks that otherwise slow teams down.

• Security matters. Features like dynamic tool scoping and permission mapping help teams feel comfortable putting AI into their production stack.

We expect adoption to grow as MCP solidifies its role as an industry standard, and as more organizations explore agent-driven content operations.

Use cases in action

The possibilities are broad, but here are some of the most compelling ways MCP could be applied (with more emerging all the time):

• Content operations at scale: Prompt an agent to update metadata, tag entries, or publish/unpublish content across spaces.

• Mass migration projects: Move content from another CMS into Contentful, with AI handling bulk transfers and transformations.

• Quality assurance and SEO: Scan entries for broken links, consistency, accessibility, or compliance issues, and generate recommendations.

• Content modeling support: Ask an agent to create or update content types, saving time on repetitive setup work.

• Asset management: Generate alt text, categorize assets, or clean up unused media files.

• Digital experiences drafting: Use AI to spin up draft entries across multiple content types for a new product launch, complete with metadata and localization placeholders

“The Contentful MCP makes repetitive content management tasks significantly easier, saving me from mountains of manual updates. Beyond that, the possibilities it unlocks — like natural language queries, content generation, and even multi-system orchestrations — are huge. It’s an exciting time to be working with it!”

Leah Wilson-Duff, Senior Product Manager, ZoomInfo

What early adopters are looking forward to next

While today’s MCP workflows focus on productivity gains and repetitive task automation, we’re hearing from early adopters who are already imagining what comes next.

Some see multi-agent collaboration as the next big step, with multiple MCP-enabled systems — ecommerce, CRM, creative tools, and more — working together to deliver complex experiences.

Others are pushing toward proactive AI, where agents don’t just wait for prompts but monitor performance metrics and suggest or even trigger updates.

And a few envision end-to-end content journeys, with agents planning, creating, reviewing, and publishing digital experiences while humans step in only for approvals.

A turning point for both marketers and developers

MCP has the potential to transform how both marketers and developers work — and, more importantly, how they work together. 

For marketers, it means being able to describe digital experiences in natural language and see them take shape across multiple systems. A campaign brief could become structured content: CRM insights surface customer needs, SEO tools suggest optimization, creative platforms generate visuals, and everything assembles in Contentful, ready to be refined and released.

For developers, MCP shifts the focus from maintaining integrations to composing systems. Instead of writing brittle scripts or custom plugins, they can set up workflows and toolkits that agents use to deliver what marketers describe. That might mean spinning up content models, migrating entries from a legacy CMS, or connecting Contentful with other platforms — work that directly powers the marketer’s ability to move faster and experiment more.

Together, this creates a new kind of collaboration: marketers guide strategy and direction, while developers design the foundations and guardrails that make agentic workflows reliable, secure, and scalable.

MCP bridges the gap, allowing both sides to focus on higher-value work — and making the creation of digital experiences faster, more fluid, and more collaborative than ever.

Ready to explore what’s possible with Contentful MCP?

Getting started with the Contentful MCP server is straightforward:

1. Install the server locally from our GitHub repo.

2. Connect your LLM (such as ChatGPT or Claude) to the MCP endpoint.

3. Authenticate with your CMA access token.

4. Test simple prompts like “Create a blog post entry in Contentful” to see it in action.

The Contentful MCP server is open source, and we’d love for you to try it out, experiment with new workflows, and share your ideas.

Whether you’re a developer, a product manager, or partner agency, MCP unlocks a new way to collaborate with AI — and we’re excited to see what you’ll build alongside our community.

Looking ahead

MCP is gaining traction as the “USB-C of AI” — a universal standard for connecting LLMs to business systems.

By embracing it early, Contentful customers are futureproofing their content stacks and preparing for the shift toward agent-centric digital experience orchestration.

For marketers, this means orchestrating campaigns across systems without touching a dozen tools.

For developers, it means composing applications with natural language instead of endless integration code.

For everyone, it signals a step toward faster, smarter, more collaborative digital operations.

Our open-source server is just the start. We’re working toward remote hosting options, deeper integrations with the Contentful App Framework, and expanded toolsets for even more advanced automation. Stay tuned for further updates.

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