Cloudflare has unveiled thirteen new Model Context Protocol (MCP) servers, enhancing the integration of AI agents with its platform. These servers allow AI clients to interact with Cloudflare’s services through natural language, streamlining tasks such as debugging, data analysis, and security monitoring.
An MCP server is a specialized type of server introduced by Cloudflare as part of their infrastructure to support AI agents in executing, debugging, and managing tasks securely and efficiently.
The concept of MCP servers is built around the idea of providing AI agents (such as those used in autonomous workflows or natural language interfaces) safe, controlled access to the tools and data they need to operate effectively. These servers don’t just run arbitrary workloads; they are tightly scoped, auditable environments that expose specific capabilities to AI models.
The new MCP servers from Cloudflare introduce several key features designed to enhance the capabilities of AI agents interfacing with cloud infrastructure. The thirteen new servers and what they do are described below, as detailed in Cloudflare’s blog post.
Cloudflare Documentation Server provides direct access to the most current Cloudflare Developer Documentation. It serves as a reliable reference point for developers working with Cloudflare products, offering guidance on APIs, Workers, networking configurations, Zero Trust solutions, and more. This server helps streamline development and troubleshooting by making technical documentation readily available within the development workflow.
Workers Bindings Server is designed to support the development of Cloudflare Workers applications by enabling integration with core primitives like storage (KV, R2, Durable Objects), secrets, and AI models. It allows developers to interact with these bindings as they would in production, making it easier to test and build serverless applications that depend on Cloudflare’s edge compute environment.
Workers Observability Server offers debugging and performance monitoring tools for Cloudflare Workers applications. It allows developers and operators to access logs, analytics, and error traces, providing essential insights into how Workers behave during execution. This observability helps troubleshoot bugs, optimize performance, and maintain reliable production applications.
Container Server creates sandboxed development environments on demand, giving developers isolated containers to run and test their code. These environments simulate realistic conditions without affecting local setups or production systems. It’s a convenient tool for experimentation, CI/CD workflows, and running ephemeral services safely and cleanly.
Browser Rendering Server enables headless browser functionality for fetching and rendering web pages. It can convert page content to markdown or capture screenshots, making it useful for automation workflows, content extraction, UI validation, and generating previews. This server is especially valuable for developers building tools that need to interact with web UIs programmatically
Radar Server connects to Cloudflare Radar, a global intelligence platform that provides insights into internet traffic trends, regional outages, domain scans, and overall web health. This server allows developers and network analysts to query real-time or historical data to assess internet stability, understand global events, or identify suspicious activity across the web.
The Logpush Server provides status summaries and health checks for Logpush jobs, Cloudflare’s mechanism for streaming logs to external storage destinations like AWS S3 or Google BigQuery. It ensures that critical observability and compliance data are flowing correctly, helping teams quickly identify failures or misconfigurations in their logging pipelines.
AI Gateway Server allows teams to search logs and trace prompt-response cycles made through Cloudflare’s AI Gateway. It provides detailed telemetry on AI requests, including token usage, response times, and prompt contents. This is valuable for teams building or monitoring AI-driven applications, especially those concerned with privacy, cost, and quality of AI interactions.
AutoRAG Server is used to manage and query documents stored in AutoRAG, Cloudflare’s retrieval-augmented generation systems. It lets developers list ingested documents and search across them to verify availability and correctness. This is crucial for AI applications that rely on external documents to provide accurate, context-rich responses to user queries.
Audit Logs Server grants access to audit logs that capture user actions and configuration changes across Cloudflare services. These logs are essential for security reviews, compliance reporting, and change tracking, offering a transparent record of activity within an organization’s Cloudflare environment. Reports can be generated and filtered for easier review.
DNS Analytics Server offers tools to analyze DNS resolution performance and troubleshoot configuration issues. It provides data such as query volumes, response times, and error rates, helping network administrators identify bottlenecks, optimize routing, and ensure DNS infrastructure is functioning efficiently and securely.
Digital Experience Monitoring (DEM) Server delivers insights into the end-user experience of web and SaaS applications. Using real-user and synthetic monitoring data, it helps organizations track performance metrics like latency, uptime, and error rates across geographic locations. This server supports IT teams in ensuring application reliability and user satisfaction.
Lastly, Cloudflare One CASB Server functions as a Cloud Access Security Broker for Cloudflare’s Zero Trust suite. It scans connected SaaS applications for security misconfigurations and potential compliance issues. This helps organizations secure their data and users by identifying risky configurations in platforms like Google Workspace or Microsoft 365, aligning with modern zero-trust principles.
The MCP server code and documentation can be found in Cloudflare’s mcp-server-cloudflare GitHub repo. These servers are accessible to any MCP client supporting remote connections, including platforms like Claude.ai. This development signifies a step towards more seamless integration between AI agents and cloud services, promoting efficiency and automation in various operational tasks.
