APIs aren’t dead. Here’s where MCP fits alongside them.

Understanding the fundamentals

APIs provide structured endpoints that let one system request data from another or trigger specific actions. When a team’s monitoring tool queries the database for metrics or when a CI/CD pipeline tells the deployment service to roll out new code, that’s an API at work.

MCP represents a different strategic approach. It’s a protocol designed specifically for connecting AI assistants and agents to external data sources and tools (including other AI tools) through a standardized interface. MCP doesn’t replace APIs. It creates a standardized layer through which AI agents can access the context they need from multiple tools and vendors.

In incident management, cross-tool access is crucial. Responders need a connected view across alerts, changes, communications, service ownership, and customer impact.

“MCP doesn’t replace APIs. It creates a standardized layer through which AI agents can access the context they need from multiple tools and vendors.”

Here’s a summary of the differences between these two concepts and some pros/cons of each:

APIs for deterministic workflows

For incident management, APIs work best for repeatable actions that must be fast and consistent, even at high volume. Another way to think about it is in terms of deterministic workflows. Incident response teams want fast, certain action with no wiggle room for AI interpretation, especially during the mitigation phase. MCP may introduce unnecessary risk.

API-based integrations also help organizations meet security requirements via explicit authentication flows, detailed audit logs, and granular permission controls – critical insight for SOC2 compliance. While MCP can use the same authorization and permissions the APIs already have, teams need an extra layer of safety with a human in the loop because an AI agent is choosing what to run.

MCP for non-deterministic paths

MCP becomes compelling when human operators interact with systems through natural language, especially during triage, diagnosis, and investigation. These are the moments where responders need to gather context from across the stack, but are slowed down by tool sprawl. MCP gives AI agents a standardized way to access distributed context, creating a strategic advantage over agents operating in isolation.

“MCP becomes compelling when human operators interact with systems through natural language, especially during triage, diagnosis, and investigation.”

For example, teams can ask, “Why are checkout errors spiking in the EU?” The AI agent can pull current incident details from the incident management platform, including event data from monitoring tools, recent change events and incident history, and information and stakeholder updates from collaboration tools like Slack or Microsoft Teams. With this context gathered and available in a single place, it’s far easier to surface likely contributing factors or hypotheses, as well as next best steps to verify.

MCP also excels in dynamic, exploratory scenarios where the sequence of steps is user-driven and approved rather than predefined. Requests can include “Post a status update about the checkout issue to our comms channel and add a quick note about impacted business services to the incident.” The agent can do both without the engineer leaving their tool of choice. It proposes a draft, checks with a human before posting, and then completes the actions upon approval.

The world will change

MCP may well represent how technical systems interconnect in an AI native future. As organizations increasingly rely on AI assistants for operational work, standardization and interoperability become invaluable. For incident management, MCP offers a path to help teams realize greater strategic value from their AI investments by giving agents access to richer context, making them more powerful than when acting in isolation, reducing tool sprawl, and improving the responder experience.