PersonalizationMCP

Overview

PersonalizationMCP is a unified personal data hub built on the Model Context Protocol (MCP). It aggregates user‑centric data from a wide range of popular platforms—Steam, YouTube, Bilibili, Spotify, and Reddit—into a single, MCP‑compatible interface. By exposing these data streams as resources and tools, the server enables AI assistants to fetch real‑time information about a user’s gaming library, music tastes, video preferences, and social media activity. The result is a more context‑rich dialogue experience where the assistant can reference recent games played, suggest new tracks based on listening history, or surface Reddit posts that match the user’s interests—all without manual data curation.

The server solves a common pain point for developers building personalized AI experiences: the fragmented nature of third‑party APIs. Each platform has its own authentication flow, rate limits, and data model. PersonalizationMCP abstracts these differences by handling OAuth2 token management, providing a single set of endpoints that return normalized JSON structures. This simplifies client code and reduces the risk of token expiration errors, as the server automatically refreshes tokens when needed. Developers can therefore focus on crafting conversational logic rather than plumbing.

Key capabilities include:

• Multi‑platform integration: Steam for game stats, YouTube and Bilibili for video metadata, Spotify for music libraries, and Reddit for social content.
• Automated OAuth2 handling: The server detects expired tokens, refreshes them, and stores new credentials, eliminating manual re‑authentication steps.
• Rich resource catalog: Each platform exposes resources such as user profiles, playlists, and activity feeds that can be queried via MCP.
• Tool support: Actions like “add track to playlist” or “unfollow subreddit” are exposed as tools, allowing an assistant to modify user data directly.
• Contextual sampling: The server can provide recent activity logs, enabling the assistant to generate contextually relevant suggestions.

Typical use cases include:

• Personalized gaming assistants that recommend new titles based on playtime or achievements.
• Music discovery bots that curate playlists from listening history and social tags.
• Video recommendation engines that surface content aligned with watch patterns across multiple platforms.
• Social media summarizers that pull recent Reddit posts or YouTube comments to keep users informed.

Integration into an AI workflow is straightforward: a client adds PersonalizationMCP as a resource provider in its MCP configuration, authenticates once with each platform, and then queries or invokes tools as needed. The assistant can seamlessly blend static prompts with dynamic data fetched from the hub, resulting in conversations that feel truly tailored to the user’s digital life.