Most AI agents today lack context. They can generate responses and analyze text, but they often fall short when it comes to understanding the systems they’re meant to support.
Coralogix’s new MCP Server aims to fix that by giving agents direct access to observability data like logs, metrics, traces, and real user monitoring, all in real time.
Built on the open Model Context Protocol (MCP) standard developed by Anthropic, the Coralogix MCP Server acts as a secure endpoint that third-party agents can connect to. Once integrated, AI agents can query Coralogix’s data stores using natural language to surface custom attributes, root causes, or business impacts. It brings the kind of system-specific context that most AI models simply don’t have.
From Ideas to Action: What Developers Are Building with MCP
“Our official MCP Server flips the table for agent builders by turning live observability data into a first-class, one-call resource,” told
Liran Hason, VP of AI at Coralogix to MSSP Alert. “Instead of cobbling together unsafe community MCP repos, developers get an official, production-grade gateway that streams logs, metrics, and traces at large scale.”
Early use cases are already showing up across developer and SRE teams. In one scenario, developers using Cursor can jump from error stack traces directly to root-cause traces inside their IDE, letting a language model suggest fixes without leaving the editor. In another, SREs have wired Claude into their workflows as a “production oracle,” asking natural language questions like "Why did checkout spike in 502s at 14:05?” and getting real-time answers via MCP. Some customers are even integrating MCP into in-house bots that detect anomalies, roll back deployments, or auto-tune infrastructure - cutting out the need for human intervention entirely.
“Give an agent trusted, real-time telemetry and it stops guessing - it acts with confidence,” Hason added. “That’s the game-changer MCP brings to the table.”
A Foundation for the Future of AI-Driven Ops
The launch also marks a longer-term shift for Coralogix’s AI strategy. While the immediate focus is on helping customers build more capable agents, the infrastructure has broader ambitions.
“MCP is simultaneously the landing pad for our long-range AI strategy and an immediate boost for customers who want their agents to see, understand, and act on live observability data,” said Hason. “Today, it equips agents with deep production context. Tomorrow, it’s the foundation for a network of self-governing AI services that all speak the same language and follow the same rules.”
Of course, giving AI direct access to system data raises questions about control and security. Coralogix acknowledges the tradeoffs and points to its existing authorization controls as a baseline.
“Coralogix already lets you separate logs among different environments, allowing you to separate sensitive data from users that are not supposed to see it. The MCP works with the same authorization Hason explained. “The risk remains the same as human users accidentally copy-pasting sensitive information to AI chatbots. It’s up to the user building the agent to use MCP responsibly and apply the right guardrails.”
