Catalyst Center MCP Server

Overview

The Catalyst Center MCP Server is a purpose‑built bridge between AI assistants such as Claude and Cisco’s Catalyst Center (formerly DNA Center). It exposes a rich set of tools that let an AI client query the network controller for device, site, interface, and client information without exposing raw API calls. By handling authentication, pagination, and data formatting internally, the server removes boilerplate from developers, enabling them to focus on higher‑level workflow logic.

Solving a Common Pain Point

Network operators often need to retrieve up‑to‑date inventory or troubleshoot connectivity issues across dozens of switches, routers, and wireless access points. Accessing Catalyst Center’s REST APIs directly requires careful token management, rate‑limit handling, and complex query construction. The MCP server encapsulates these details behind simple, declarative tool calls. An AI assistant can now answer questions like “Show me all devices with hostname containing ‘switch’” or “List clients connected to SSID ‘Corporate‑WiFi’” with a single request, while the server handles authentication and data aggregation.

Core Value for AI‑Driven Development

For developers building AI‑augmented network management solutions, this server offers a turnkey integration point. By configuring the MCP server once in the Claude Desktop client, any subsequent prompt can invoke network queries without writing custom API wrappers. The server’s tools are intentionally granular—, , , etc.—so that AI assistants can compose complex queries from simple building blocks. This modularity also means developers can extend the server with new tools or modify existing ones without changing client code.

Key Features and Capabilities

• Secure, token‑based authentication with Catalyst Center credentials stored in a file.
• Device discovery and filtering, including status checks, hostname patterns, and IP lookup.
• Site hierarchy traversal to retrieve buildings, floors, or specific sites along with associated devices.
• Interface enumeration and status reporting, supporting queries for down interfaces, specific interface types, or IP‑assigned ports.
• Client management, providing counts, detailed MAC‑address lookup, SSID filtering, and time‑range queries.
• Time‑range helper that converts user‑friendly date ranges into API‑compatible timestamps, simplifying temporal queries.
• FastMCP foundation, giving the server robustness, extensibility, and a standardized transport layer.

Real‑World Use Cases

• Rapid troubleshooting: An AI assistant can instantly list all unreachable devices or down interfaces, allowing operators to pinpoint issues before escalating.
• Network health dashboards: By querying client counts and device status, developers can build conversational dashboards that answer questions like “How many clients are connected to the corporate Wi‑Fi?” or “Show me devices with firmware below 5.0.”
• Compliance and audit: The server can fetch site hierarchies and device inventories, enabling automated compliance checks against organizational policies.
• Operational automation: Combined queries such as “Show me all devices in site X and their interfaces” let scripts trigger remediation actions (e.g., reboot a device) based on AI‑derived insights.

Integration with AI Workflows

Once the MCP server is registered in Claude Desktop, any prompt that requires network data can be satisfied by invoking the relevant tool. The AI’s natural language understanding maps user intent to a specific tool, passing parameters extracted from the prompt. The server returns structured JSON that Claude can embed directly into responses or use to trigger further actions, such as sending a notification to Slack or updating a CMDB. This seamless loop—prompt → tool call → structured data → response—makes network management conversational and reduces the cognitive load on operators.

Unique Advantages

What sets this MCP server apart is its tight coupling with Cisco Catalyst Center’s native API ecosystem, combined with a developer‑friendly configuration model. The use of FastMCP ensures low latency and reliable streaming, while the ‑based setup keeps credentials out of source control. Developers can therefore prototype and deploy AI‑enabled network tools with minimal friction, scaling from a single site to enterprise‑wide deployments without rewriting core logic.