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MCP Server Explained – The Hype and Impact on Developers

Jul 1, 2025 | Episode

https://youtu.be/damXfAW5x9g

Revolutionizing AI Agent Development: The Power of Remote MCP Servers with Cloudflare

The world of artificial intelligence is evolving at an unprecedented pace, with AI agents leading the charge as the future of AI. These intelligent, autonomous systems are designed to make decisions, adapt to changing environments, and perform tasks on a user’s behalf. However, building scalable and cost-efficient AI agents has historically presented significant challenges for businesses. Enter the Model Context Protocol (MCP) server, a fast-growing open-source standard that is fundamentally changing how AI agents interact with external services and tools. Cloudflare, a leading connectivity cloud company, is at the forefront of this revolution, offering the industry’s first remote MCP server capabilities to accelerate AI agent development from months to minutes.

What is an MCP Server and Why Does It Matter?

At its core, MCP is an open-source standard that enables AI agents to directly interact with external services. This is a game-changer because it moves AI beyond simply giving instructions to actively completing tasks, such as sending emails, booking meetings, or deploying code changes. As Ricky Robinette, who leads developer relations at Cloudflare, puts it, MCP is like “the old man in the game “Legends of Zelda” in the cave saying, ‘It’s dangerous to go alone. Take this.'”—it provides AI with tools to access data and functionality it wouldn’t have otherwise.

Previously, MCP was largely limited to running locally on a device, which, while accessible to early adopters, hindered its wider mainstream adoption and raised security concerns due to potential unrestricted access to local systems. The ability of an MCP server to connect to any Large Language Model (LLM) and provide tool access without relying on closed-source or application clients is a key feature. MCP-Use, an open-source client library, aims to make it even easier to interact with MCP servers using custom agents.

Cloudflare’s Pioneering Approach to Remote MCP Servers

Cloudflare recognized the critical need for a secure and scalable way to deploy MCP servers. Their announcement of the industry’s first remote MCP server is a significant step forward. By enabling developers to build and deploy remote MCP servers on their network, Cloudflare addresses the security challenge by isolating the agent’s access to a controlled API, rather than the entire local system. This shift also means MCP servers built on Cloudflare can maintain context, offering a persistent user experience. Furthermore, Cloudflare simplifies authentication and authorization for secure agent deployment through partnerships with companies like Auth0, Stytch, and WorkOS, allowing users to delegate permissions to agents easily.

Cloudflare’s Developer Platform, built on one of the world’s largest and most interconnected networks, is designed specifically for building and scaling AI agents. Their network allows code to run within 50 milliseconds of 95% of online users, ensuring fast, low-latency responses for AI agents. This infrastructure is crucial for scaling agentic systems cost-effectively.

Core Components for Building AI Agents on Cloudflare

Cloudflare provides several offerings that address the critical challenges in building robust AI agents:

  • Durable Objects (Now on Free Tier): A special type of Cloudflare Worker, Durable Objects combine compute with storage, enabling developers to build stateful applications in a serverless environment without managing infrastructure. This is vital for AI agents that need to remember past preferences or adapt behavior based on prior events, maintaining context across interactions. The inclusion of Durable Objects in a free tier democratizes access to this essential component.
  • Workflows (Now Generally Available): Workflows facilitate the creation of durable, multi-step applications that can automatically retry, persist, and run for extended periods. This is particularly useful for complex agent tasks, such as an agent that searches for flights, purchases them, and sends confirmations, requiring persistent operations over time.
  • Cost-Efficient AI Deployment: AI inference, unlike training, has unpredictable and inconsistent demand due to human behavior. Traditional cloud providers often require provisioning for peak capacity, leading to unnecessary costs. Cloudflare’s serverless platform automatically scales AI agent resources and inference based on demand, from zero to global scale in milliseconds, ensuring organizations pay only for what they use and dramatically reducing costs. RedMonk senior analyst Kate Holterhoff notes that Cloudflare’s developer-friendly ecosystem, including the free tier for Durable Objects and serverless AI inference options, can empower more businesses to adopt and experiment with agentic AI. Cloudflare also deploys GPUs globally across over 190 cities to provide low-latency AI experiences.
  • AI Gateway and Workers AI: Beyond the core components, Cloudflare also offers specialized products like AI Gateway for observing and controlling AI applications, and Workers AI for running machine learning models directly on their network.

Real-World Impact and the Future of Development

The practical applications of MCP servers with Cloudflare’s platform are already evident. Examples include Asana’s demonstration of turning meeting notes into structured project plans by automatically filing tickets. In the commerce sector, Square has shown significant interest. Internally, Cloudflare uses AI agents to analyze D1 databases and troubleshoot issues by sifting through logs for patterns, greatly assisting Site Reliability Engineers (SREs) in reducing “toil” and quickly diagnosing problems.

The adoption of MCP servers is seen as a potential “pivot point for development in general,” particularly for agentic AI development. While the technology is still in its early stages, having only been widely discussed for months or even weeks, it is enabling developers to move from “months to minutes” in code development. It streamlines workflows by offloading tool calling to an MCP server, allowing developers to build AI tools faster and more efficiently. This shift addresses the “adapt or die” reality in technology, encouraging developers to embrace new ways of building.

Cloudflare emphasizes that their tools are designed to help developers move fast and go from “zero to one,” especially for well-scoped and defined problems that benefit from automated assistance.

Getting Started with Cloudflare and MCP Servers

To explore Cloudflare’s offerings for agentic AI and MCP server development, you can visit agents.cloudflare.com. For demos and deployable examples, check out their GitHub repository at github.com/cloudflare/ai. Developers are also encouraged to join the Cloudflare Discord community (discord.cloudflare.com) to engage in discussions, share what they’re building, and provide feedback that can influence future developments in this rapidly evolving space.

The Model Context Protocol, especially with the added capabilities of remote MCP servers from Cloudflare, is indeed transforming the landscape of AI agent development, making powerful, autonomous systems more accessible, secure, and efficient to build and deploy.

Check out this other episode featuring an interview with Cloudflare.

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