GenieACS-MCP

GenieACS‑MCP is a lightweight bridge that turns any GenieACS instance into an MCP v1 server. By exposing the ACS’s data and control plane through a single JSON‑RPC endpoint, it lets large language models (LLMs) and autonomous agents read device information and trigger CPE actions without needing to understand the intricacies of the ACS API. This integration removes a major friction point for developers building automation or monitoring workflows that involve network devices managed by GenieACS.

The server provides two main MCP concepts: resources and tools. Resources are read‑only references to GenieACS data such as a specific device (), configuration files, or task records. Tools expose operational commands that can be invoked on a CPE—examples include , , and . Because everything is reachable through the same endpoint, an LLM can simply , call to pull the latest state, list available tools with , and then execute an action via . This pattern mirrors the way developers normally interact with GenieACS through its web UI or REST API, but it is now consumable by AI assistants in a standardized way.

Key capabilities of GenieACS‑MCP include:

• Unified JSON‑RPC interface: All ACS interactions are wrapped in MCP calls, eliminating the need for custom adapters.
• Session management: The server issues a session ID on , which the client must include in subsequent requests, ensuring secure and stateless operation.
• Extensibility: By adding new GenieACS endpoints or custom logic, developers can expose additional resources or tools without changing the MCP contract.
• Language‑agnostic integration: Any LLM or agent that understands MCP can talk to GenieACS, making it a drop‑in component for diverse AI pipelines.

Typical use cases involve automated firmware rollouts, proactive health checks, or policy‑driven configuration changes. For example, an AI assistant could monitor device telemetry, detect a firmware drift, and trigger automatically. In a customer support scenario, an LLM could read the latest device state and suggest troubleshooting steps or even reboot the CPE on demand. Because GenieACS is widely used in ISP and enterprise networks, this MCP bridge empowers AI systems to manage real‑world network infrastructure with minimal manual scripting.

Overall, GenieACS‑MCP offers a clean, standards‑based pathway for AI assistants to orchestrate network device management tasks. Its minimal footprint, built in Go, and straightforward configuration make it an attractive choice for developers who want to fuse GenieACS automation with modern LLM workflows.