Spotify Playlist Curator MCP Server

The Spotify Playlist Curator MCP Server bridges the gap between your Spotify library and Claude AI, enabling developers to build intelligent music‑curation workflows. By exposing a set of MCP methods that retrieve playlist data, analyze audio features, and generate recommendations, the server empowers AI assistants to transform a static collection of tracks into a dynamic, mood‑aware listening experience.

What Problem Does It Solve?

Curating a playlist that matches a specific vibe or energy level is traditionally a manual, time‑consuming task. Musicians, DJs, and casual listeners alike struggle to balance tempo, key, mood, and genre across dozens of tracks. The server automates this process: it pulls raw track data from Spotify, applies audio‑feature analysis (e.g., BPM, energy, valence), and feeds those insights into Claude AI. The model then produces tailored song suggestions that fit the existing musical context, eliminating guesswork and accelerating playlist creation.

How It Works for Developers

The MCP interface offers a clean, declarative set of methods:

• and expose the user’s Spotify data, including a summary of mood, energy, and tempo derived from the API.
• sends that summary to Claude, which returns a ranked list of tracks that complement the playlist’s character.
• and let developers programmatically update or create Spotify playlists with the AI‑generated selections.
• provides a fallback for manual exploration, allowing developers to query Spotify directly from the MCP client.

Because these methods are accessible via standard MCP calls, any AI assistant that supports the protocol can integrate playlist curation into its conversational flow. For example, a chatbot could ask a user for the desired mood and then automatically generate or update a playlist without exposing the underlying Spotify authentication steps.

Key Features & Capabilities

• Audio Feature Analysis – Leverages Spotify’s audio‑feature API to quantify mood, energy, tempo, and more.
• Claude‑Powered Recommendations – Uses natural language understanding to interpret playlist characteristics and suggest complementary tracks.
• Full Playlist Lifecycle Management – Create, read, update, and search playlists directly through MCP.
• User‑Friendly Authentication – Handles OAuth flow via a simple browser redirect, keeping the developer’s codebase clean.
• Extensible Design – The server’s modular structure allows additional methods (e.g., genre filtering, popularity thresholds) to be added with minimal friction.

Real‑World Use Cases

• Personal Music Assistants – A conversational AI can ask for a “late‑night chill” playlist and deliver a ready‑to‑listen setlist in seconds.
• DJ Set Preparation – DJs can input an existing mix, receive BPM‑aligned recommendations, and auto‑populate a new setlist.
• Content Creators – Podcasters or vloggers can generate background music that matches the tone of their episode.
• Music Discovery Platforms – Websites or apps can offer AI‑curated playlists as a premium feature, enhancing user engagement.

Unique Advantages

Unlike generic recommendation APIs, this server couples analysis and generation in a single workflow. The AI receives concrete, quantitative insights about the user’s current collection, allowing for nuanced suggestions that respect tempo and mood continuity. Moreover, because it operates through MCP, developers can embed the curator into any AI assistant without worrying about REST endpoints or authentication plumbing—just a few declarative calls and the server handles the rest.