Unifi MCP Server

Overview

The Unifi MCP Server is a lightweight, purpose‑built Model Context Protocol (MCP) server that exposes the configuration and telemetry of Ubiquiti’s Unifi network controller to AI assistants. By turning a Unifi site into a first‑class MCP resource, developers can query device status, network health metrics, and perform management tasks directly from an AI‑powered interface. This eliminates the need for manual dashboard inspection or custom API wrappers, enabling rapid automation and intelligent monitoring.

Problem Solved

Network operations teams often rely on the Unifi Controller’s web UI or REST endpoints to troubleshoot connectivity issues, audit device compliance, or trigger firmware updates. These interactions are typically manual and siloed from modern AI workflows. The Unifi MCP Server bridges that gap by presenting the controller’s data as structured, discoverable resources. Developers can now ask an AI assistant questions like “Show me all APs with low signal strength” or “What is the current bandwidth usage on site B?”, and receive real‑time, contextual answers without leaving their development environment.

Core Functionality

• Resource Discovery: The server automatically registers Unifi devices, sites, and interfaces as MCP resources. Each resource includes a descriptive schema that the AI can introspect to understand available fields.
• Tool Exposure: Operations such as rebooting a device, updating firmware, or toggling guest network settings are exposed as callable tools. The AI can invoke these actions with confidence, knowing the required parameters and expected outcomes.
• Prompt Context: The MCP server supplies contextual prompts that embed network topology information, recent alerts, and configuration summaries. This allows the AI to generate more relevant suggestions or troubleshooting steps.
• Sampling & Logging: Built‑in sampling ensures that responses are concise yet informative, while logging captures all interactions for auditability—a critical requirement in regulated environments.

Use Cases

• Automated Troubleshooting: An AI assistant can diagnose connectivity problems by querying device health metrics, suggest configuration changes, and even apply them automatically.
• Capacity Planning: By aggregating bandwidth usage across sites, the AI can forecast future load and recommend hardware upgrades.
• Security Audits: The server can expose compliance data (e.g., firmware versions, open ports) so the AI can generate audit reports or trigger remedial actions.
• DevOps Integration: Continuous integration pipelines can query the Unifi MCP Server to validate network state before deploying application updates, ensuring that infrastructure constraints are respected.

Integration with AI Workflows

Developers embed the Unifi MCP Server into their existing MCP client configurations (e.g., VSCode’s ). The server runs as a local process, exposing its endpoints over the MCP protocol. AI assistants then treat Unifi resources just like any other data source: they can list available sites, retrieve device attributes, and invoke management tools. Because the server adheres to MCP’s standardized schema conventions, it plugs seamlessly into any AI platform that supports MCP, such as Claude or other custom assistants.

Unique Advantages

• Zero‑Configuration API Keys: The server leverages the Unifi controller’s integration tokens, eliminating the need for custom OAuth flows.
• Native Support for Multiple Sites: A single instance can expose multiple Unifi sites, simplifying multi‑site management.
• Lightweight and Secure: Built with Python’s async ecosystem, the server consumes minimal resources while enforcing HTTPS and API key authentication.
• Extensible Architecture: Developers can add new tools or modify resource schemas without altering the core MCP interface, making it future‑proof as Unifi’s API evolves.

In summary, the Unifi MCP Server transforms a legacy network controller into an AI‑friendly data source, empowering developers to build intelligent, automated, and secure network operations workflows.