Raid Shadow Legends MCP Server

Raid Shadow Legends MCP Server

The Raid Shadow Legends MCP Server bridges the gap between an AI assistant and the mobile game Raid Shadow Legends by leveraging Tasker automation. It exposes a Mission Control Protocol (MCP) interface that lets an AI client issue high‑level game commands, set automation goals, and receive real‑time status updates—all without touching the device directly. This is especially useful for developers building AI‑driven game bots, monitoring dashboards, or integrating gameplay into larger workflows.

At its core, the server connects to an MQTT broker that serves as the communication backbone with a Tasker project running on an Android device. The broker handles publish/subscribe messaging for three key topics: , , and . When the AI sends a command through the MCP tool RaidShadowLegendsControl, the server forwards it to Tasker via MQTT. Tasker parses the message, executes the corresponding action (e.g., launching the app, starting a clan boss fight), and then publishes updated status back to . The AI can query this status through the RaidShadowLegendsStatus tool or the REST endpoint , receiving a JSON payload that includes raid progress, energy levels, and battle counts.

The server also provides RaidShadowLegendsSetGoal, allowing an AI to define automation objectives such as “complete 20 campaign battles” or “auto‑sell after every 5 fights.” These goals are translated into MQTT messages that Tasker listens for, enabling the device to autonomously manage energy refills or battle selection based on the specified parameters. This level of abstraction lets developers focus on higher‑level logic—like dynamic goal adjustment or multi‑game orchestration—without needing to understand the intricacies of Tasker scripting.

Real‑world use cases abound: a gaming analytics platform can schedule raid sessions, monitor performance metrics, and trigger alerts when thresholds are crossed; a content creator might automate repetitive battles to generate in‑game footage on demand; or a research project could study AI decision‑making by feeding game state data back into reinforcement learning loops. Because the MCP server exposes both REST and native MCP tools, it fits seamlessly into existing CI/CD pipelines or serverless architectures that already consume REST APIs.

Unique advantages of this MCP implementation include its MQTT‑based decoupling, which ensures low latency and reliable message delivery even across network boundaries; its goal‑oriented automation that abstracts complex Tasker logic into simple JSON payloads; and its extensibility, allowing future features such as authentication, richer status reporting, or a web dashboard to be added without disrupting the core protocol. For developers familiar with MCP concepts, this server offers a ready‑made bridge to Raid Shadow Legends that can be integrated into larger AI ecosystems with minimal friction.