Mike Vizard reports on Sentry’s new capability for monitoring MCP servers, explaining how this feature can help DevOps teams manage, secure, and optimize AI-driven application workflows.

Sentry Adds Tool for Monitoring MCP Servers to APM Platform

By Mike Vizard

Sentry has expanded its application performance monitoring (APM) platform to include monitoring for Model Context Protocol (MCP) servers. Originally created by Anthropic, MCP has rapidly become a standard way for AI-powered applications to access and interact with data—growing in popularity among development and DevOps teams.

According to Cody De Arkland, Sentry’s senior director of developer experience, developers are increasingly incorporating AI services into their applications. Many rely on MCP servers to facilitate these integrations. Sentry’s new MCP Server Monitoring tool, accessible via a JavaScript SDK, offers DevOps teams visibility into crucial aspects like server performance, traffic load, AI client usage, and top-called (or problematic) tools.

Key Capabilities

  • Traffic and Usage Monitoring: Track which AI tools are most used, slowest, or failing, and diagnose issues at the input level.
  • Debugging Support: Equip DevOps teams to quickly pinpoint and resolve MCP-related problems during the AI development lifecycle.
  • Security and Observability: Enhance detection of abnormal events or misuse, helping mitigate security risks introduced by MCP’s central role in data access and workflow automation.

Why Monitoring MCP Matters

MCP servers operate at the top of the software stack, moving business logic—context, state, and intent—between application components through APIs carried over HTTP/S. Unlike stateless protocols, MCP includes state in every message, turning context into both payload and target. This design removes the need for custom integrations between every model and every data source, offering a scalable method for AI systems to interact with varied external data.

However, this centralization also creates new security risks: if compromised, entire business workflows and sensitive data could be exposed. Continuous monitoring and deep observability become essential for DevSecOps teams.

Impact on DevOps Workflows

  • Re-engineering Required: As MCP becomes foundational for AI workflows, DevOps teams will need to adapt automation, deployment, and observability strategies to account for MCP-based interaction models.
  • Opportunity for Better AI Applications: Organizations that use monitoring proactively can improve reliability, security, and complexity management as AI features proliferate.
  • Security Emphasis: Monitoring provides early warning of misuse and supports robust risk mitigation, helping prevent MCP from becoming a soft spot in enterprise architecture.

Conclusion

As the adoption of MCP and AI in applications accelerates, tools like Sentry’s MCP Server Monitoring add a much-needed layer of visibility and control. These capabilities enable DevOps and security (DevSecOps) teams to tackle the operational and cybersecurity challenges of integrating AI into complex software workflows.

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