SAN FRANCISCO, May 27, 2025 -- groundcover, the eBPF-driven observability platform for modern architectures, launches an MCP server that is faster and more accurate than any other MCP server on the market. Starting today, developers can infuse their AI-driven development with deep system context from groundcover's granular access to logs, metrics, and traces empowered by eBPF.
"AI is transforming software development and enabling teams to move faster than ever – but to ensure accuracy and reliability, AI needs the same deep context developers rely on," said Orr Benjamin, Head of Product at groundcover. "By integrating with groundcover's MCP server, tools like Cursor gain unparalleled visibility into the system and architecture, empowering AI to deliver code that's not just fast but also production-ready."
Developers are now deploying code 70% faster with AI assistance according to the latest DORA report. However, there are myriad day two problems that come with this tremendous boost in output, leaving platform engineering and security teams struggling to keep up. By plugging groundcover's new MCP server directly into an IDE like Cursor, hallucinations will be drastically reduced, and the quality of the code dramatically improved.
Now developers working with AI applications can take advantage of all of the rich data already within groundcover including identified errors, problematic logs, cloud infrastructure issues, traces with specific customer IDs, the topology of communication of services, and more. The company's new MCP server bakes all of this critical context into every piece of the software development lifecycle – from code generation to incident response – setting a new standard for LLM-driven observability that ensures quality, security, and performance.
About groundcover
groundcover is a cloud-native application monitoring solution that reinvents the domain with eBPF. Built for modern production environments, it enables teams to instantly monitor everything they build and run in the cloud without compromising on cost, granularity, or scale.
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