Atris MCP for Audius

Atris MCP for Audius – A Unified AI Interface to the Audius Music Platform

The Atris MCP server brings the entire Audius ecosystem into a single, language‑model friendly interface. By exposing nearly all of the Audius Protocol API through 105 thoughtfully designed tools, it removes the need for developers to write custom SDK wrappers or handle authentication flows manually. Instead, an LLM can issue high‑level natural language commands—“Recommend underground jazz artists” or “Show me the analytics for my latest track”—and receive structured, actionable responses. This capability is especially valuable for creators and curators who want to harness AI to streamline content discovery, management, and monetization without leaving the familiar conversational context of an assistant.

At its core, the server offers three main types of resources: tracks, users, and playlists (including albums). Each resource is represented as a structured object that can be queried, updated, or streamed. The tool set supports a wide range of operations: from simple lookups (“Find tracks with BPM > 120”) to complex workflows such as uploading new music, configuring NFT gating, or scheduling marketing campaigns. The inclusion of audio streaming tools lets developers stream tracks directly into their applications or open them in the Audius Desktop client, enabling real‑time listening experiences within AI‑driven workflows.

Key features include:

• Comprehensive API coverage – over 95 % of the Audius API is available, allowing developers to perform nearly any action that the native platform supports.
• Natural‑language tooling – prompts and tool signatures are designed for conversational use, so a user can request “Create a playlist of upbeat electronic tracks” and the server translates that into precise API calls.
• Analytics and monetization insights – tools expose play counts, listener demographics, tipping history, and revenue streams, empowering creators to make data‑driven decisions.
• Social interaction management – follow users, comment on tracks, and identify influential fans are all possible through simple commands.
• Workflow automation – the server can schedule releases, update playlists based on listening habits, and generate marketing timelines, making it a powerful assistant for production pipelines.

In practice, this MCP server is ideal for music‑tech startups building AI‑powered discovery apps, independent artists seeking automated content management, or data analysts exploring audience metrics. By integrating the server into an AI workflow, developers can layer sophisticated music‑industry logic on top of conversational interfaces, dramatically reducing development time and opening new avenues for creative automation.