The Role of MCP in Scaling Agentic Network Automation

TL;DR:

Modern networks are complex, with every vendor using different syntax, making it difficult for AI to interact with devices and automate networks at scale. MCP solves this by:

• Acting as a translator between AI agents and network devices

• Allowing AI to dynamically discover available tools at runtime

• Enabling scalable, multi-vendor automation with less complexity

• Letting teams extend capabilities simply by adding new MCP servers

In large, distributed network environments, every vendor and system tends to have its own way of being accessed and controlled. Some rely on command-line interfaces, others on structured APIs, and even when the functions are similar, the syntax and protocols often vary widely. This diversity creates a major operational challenge for AI systems tasked with interacting with the network, whether for troubleshooting, configuration, or other tasks. If AI is expected to operate across all of these systems without being hand-coded for each one, it needs a structured way to discover and use available tools dynamically. This is where the Model Context Protocol (MCP) comes into play. In our networking use case, AI agents connect to MCP servers as clients using the JSON-RPC 2.0 message format. For the transport mechanism, MCP leverages either STDIO for local communication or HTTP, with optional SSE, for remote communication. Each MCP server exposes a structured set of tools, such as retrieving interface counters, inspecting routing tables, or running health checks, without binding the AI agents to specific devices or command syntax. Each server advertises the tools it can execute, along with standardized input parameters required for each. In other words, the MCP server acts as a translator, taking a request from the AI agent and mapping it to the appropriate downstream tools to communicate with the device or system. This allows the AI agents to dynamically discover what tools are available at runtime, rather than relying on fixed syntax baked into their design.

The MCP model introduces a clean separation between the agent’s reasoning and the underlying operational actions. Instead of the AI system needing to know how to directly interact with different types of devices, for example, a Cisco router, a Juniper switch, or a SONiC-based platform, it simply selects the best available tool based on the context of the problem. The MCP server is responsible for translating that selection into the appropriate command execution, API interaction, or protocol operation using whatever automation framework or communication method is best suited for the device type.

This approach enables agentic systems to scale across diverse environments without increasing internal complexity. A Cisco MCP server might execute CLI commands and parsers tailored for Cisco devices, while a Junos MCP server could interact through NETCONF. Regardless of the underlying protocol or parsing mechanism, the experience for the agent remains unified: it reasons about network problems and selects tools dynamically, without needing to manage the specifics of device communication or command syntax interpretation.

As networks continue to evolve, this architecture becomes critical. Adding a new vendor no longer means rewriting core agent logic. Instead, a new MCP server is deployed alongside existing ones, advertising its available tools to the system. AI agents discover the new capabilities automatically and can immediately begin using them as part of their troubleshooting workflows.

Over time, MCP servers will serve as modular building blocks in large-scale automation fabrics. Different teams or domains may operate their own MCP servers. For example, separate ones for campus networks, data center fabrics, and cloud edge sites. The agentic AI system can seamlessly traverse across them, selecting tools from the appropriate server based on where the incident is unfolding.

By decoupling tool discovery and execution from the agent’s core logic, MCP servers allow network automation systems to move from static scripts to more dynamic, adaptive operations. They also abstract away the device-specific complexity, reducing the cognitive load on the LLM, allowing it to focus mostly on reasoning rather than managing low-level communication or syntax details.

For organizations managing multi-vendor networks, MCP servers represent a path toward true AI-driven network operations, where automation is not just scalable, but adaptable to the real-world conditions of the infrastructure underneath.

As this protocol matures, we can expect additional layers to emerge: integrating telemetry pipelines, stateful protocol analysis, and policy-driven behaviors into the same framework. But the foundation remains the same: to give AI systems structured, real-time access to tools without embedding device and protocol complexity into the core of their reasoning.