ONOS MCP Server

The ONOS MCP Server bridges the gap between AI assistants and real‑world software‑defined networking by exposing the full breadth of the ONOS controller through the Model Context Protocol. In traditional network operations, engineers manually query REST endpoints or use CLI tools to retrieve topology, install applications, and tune flow rules. The MCP server consolidates these interactions into a single, structured API that an AI assistant can call directly. This eliminates the need for custom adapters or repetitive scripting, enabling developers to embed sophisticated network management logic into conversational agents with minimal effort.

At its core, the server translates ONOS REST calls into MCP resources. Clients can list devices, links, hosts, and intents; fetch statistics and cluster health; or modify flow tables and application states—all through a uniform JSON schema. The resource model is deliberately aligned with ONOS’s own concepts, so developers familiar with the controller can map MCP calls to their existing knowledge. The server also offers a suite of analytical tools: summary dashboards, traffic analytics, path computation, and QoS configuration. These are not just data retrieval endpoints; they perform non‑trivial calculations on the fly, returning concise insights that an AI can summarize or act upon.

The MCP server’s specialized prompts are a key differentiator. Prompts such as Network Diagnostics, Intent‑Based Configuration, and Performance Optimization encapsulate common network tasks into high‑level actions. When an AI assistant receives a user query, it can invoke the appropriate prompt to trigger complex workflows—like automatically generating an intent that connects two hosts with a specified QoS level—without the user having to understand ONOS internals. This capability is especially valuable in educational settings, where students can experiment with network policies through natural language, or in research labs that need rapid prototyping of SDN applications.

Real‑world use cases include automated incident response, where an AI assistant monitors system health and proposes remedial actions; dynamic service chaining for multi‑tenant environments, leveraging intent creation and path computation; and continuous performance tuning, using analytics to adjust flow rules or QoS policies in response to traffic shifts. By integrating seamlessly with AI workflows—through the standard MCP client configuration or via tools like Claude Desktop—the server allows developers to embed network control into broader conversational agents, orchestrating infrastructure changes alongside data analysis or customer support.

Unique advantages of the ONOS MCP Server stem from its tight coupling with a production‑grade SDN controller, its comprehensive coverage of ONOS features, and its ready‑made analytical primitives. Unlike generic network APIs, it offers intent management and application lifecycle control out of the box, enabling AI assistants to not only read network state but also shape it. This positions the server as a powerful enabler for next‑generation AI‑driven network operations, where natural language interfaces can orchestrate complex SDN logic with confidence and precision.