The rise of AI-driven development
AI is becoming a core part of how software gets built. Microsoft and Google report that around 30% of their code is now written by AI. At Y Combinator, 95% of startups in a recent batch leaned heavily on AI to generate code. Whether or not you buy into “vibe coding,” the shift is undeniable: AI is reshaping software development.
But most of the conversation stops at writing code. Operating it is another story. As AI-generated code spreads, DevOps and SRE teams are left managing increasingly complex systems, often without the context they need. This is where observability becomes critical, and where feeding that context into AI workflows could make the biggest difference. With the right signals, AI can help teams not just build faster, but run more reliably.
What is MCP? And why is it catching on?
The Model Context Protocol (MCP), introduced by Anthropic in 2024, is an open standard that connects AI applications to external systems. Instead of custom integrations for every model and tool, MCP defines a common interface: a user sends structured requests to lightweight MCP servers, which then fetch or act on data from systems like databases, monitoring platforms, or CI/CD pipelines.
MCP is frequently compared to the USB‑C connector in the AI domain, serving as a singular, universal interface through which nearly any system can connect and interact. In the same way that the USB‑C connector replaced a myriad of proprietary cables with one streamlined, standardized interface, MCP establishes a unified protocol that seamlessly links AI models with external tools within a common framework.
This standardization means that AI-driven applications no longer require custom-built adapters for each individual service or data source. Instead, any AI agent can directly communicate with any service that adheres to the MCP standard via a common protocol—just as any modern device can utilize a USB‑C connection.
This setup streamlines how AI models access external data. MCP servers operate inside the user’s environment, enforcing permissions and securing sensitive data. The result is a scalable, vendor-neutral way to deliver trusted context to AI.
Why observability data matters in an AI-first world
As AI tools move beyond code generation to helping run production systems, observability data becomes essential. Logs, metrics, and traces offer the insight teams need to debug issues, optimize performance, and maintain uptime. For AI to be a useful assistant, it needs access to this telemetry.
But raw observability data is messy, noisy, and expensive to process. Dumping thousands of log lines into an LLM rarely produces useful results. What’s needed is refined context, patterns, summaries, and key signals that highlight what matters.
Why groundcover’s MCP server redefines context delivery for AI
groundcover’s approach to MCP reflects a different mindset by tailoring observability data specifically for how LLMs consume and reason with information. Rather than passing raw logs and traces, which are often noisy, repetitive, and overwhelming, groundcover’s server pre-summarizes and structures the data. This ensures that nearly every token an LLM receives is meaningful and actionable.
The server does this through features like Log Patterns, which condense repetitive log lines into digestible formats, and Drilldown mode, which highlights statistically significant attributes from trace or log data. It also incorporates anomaly detection to surface critical spikes or outliers directly. This structured delivery dramatically improves efficiency, cuts latency and cost, and allows LLMs to follow chains of inquiry much like a human would — all while staying within token constraints.
Conclusion: a smarter foundation for AI in production
As AI takes on a larger role in software development, it must also evolve to support operations. That requires access to observability data, not in raw form, but as structured, meaningful input.
groundcover treats the AI not as a generic client, but as a special one that requires curated, context-rich inputs to be effective. This rethinking of data delivery makes AI more useful, accurate, and practical in real-world observability and troubleshooting scenarios.
