MCP is becoming the universal connector between AI agents and the tools and data they need, and it is a new surface you have to observe and govern.
MCP (Model Context Protocol) is an open standard that defines how AI applications and agents connect to external tools, data sources and context. It provides a common protocol so any MCP-compatible model or agent can use any MCP-compatible tool or data source, like a universal adapter between AI and the systems around it.
Before MCP, connecting an AI application to each tool or data source, a database, a ticketing system, a search index, meant a bespoke, one-off integration, and every model and every tool spoke its own language. MCP standardises that interface. It replaces N times M custom integrations with a single protocol, so a tool exposed once through MCP can be used by any MCP-capable agent, and an agent that speaks MCP can reach any MCP tool. Introduced by Anthropic and adopted broadly across the ecosystem, it is doing for AI-to-tool connections what a standard protocol did for the web.
MCP follows a client-server model. An MCP server wraps a tool or data source and exposes its capabilities, resources it can provide and actions it can perform, over the protocol. The AI application, the MCP client, discovers those capabilities and calls them through a standard message format. This separation means the people who own a data source can expose it safely and consistently, and the people building agents can consume many sources without writing a custom connector for each. The protocol handles discovery, invocation and the exchange of context.
As agents increasingly act through MCP, those connections become a new operational surface. Each MCP call an agent makes, which tool, with what arguments, how long it took, whether it failed, is something you need to observe, because an agent's behaviour is now partly defined by the tools it reaches. It is also a governance surface: what an agent is allowed to access through MCP, and whether those actions are audited and reversible, is exactly the kind of control that separates safe agentic operations from risky ones. Observing and governing MCP is becoming part of running agents in production.
Ops Singularity observes the tool and MCP calls agents make as part of its AI/ML and Agent Ops pillar, and governs what agents are allowed to access, with every action reversible and audited, so agentic operations over MCP stay safe and accountable.
Model Context Protocol. It is an open standard for connecting AI applications and agents to external tools, data sources and context through a common interface.
MCP was introduced by Anthropic as an open standard and has since been adopted broadly across the AI ecosystem.
Because agents increasingly act through MCP, each MCP call is a new signal to observe (which tool, latency, errors) and a governance surface to control (what an agent may access), both essential for running agents safely in production.
Ops Singularity turns open telemetry into autonomous, governed resolution. See it on your own stack.