PersonalizationMCP Details
PersonalizationMCP is a unified personal data hub built on MCP (Model Context Protocol) that enables AI assistants to access and reason over data from Steam, YouTube, Bilibili, Spotify, Reddit, and more. This repository showcases a Python-based MCP server that aggregates platform APIs, manages OAuth2 tokens, and exposes a rich set of tools to query user data, playlists, watch history, and social actions. It emphasizes local data handling, token management automation, and a modular architecture that makes it easy to add new platforms through the @mcp.tool() decorator and server integration. Ideal for developers building context-aware assistants who want a single, extensible backend to surface personal data across multiple services.
The MCP server is designed to run locally on your machine with secure configuration, offering multiple installation paths (conda, uv, or pip with virtualenv). It includes a comprehensive set of available tools organized by platform, robust token management (notably YouTube), and practical guidance for configuration, testing, and cursor-based integration with consumer apps like Cursor. The project also provides detailed setup steps for each platform, including how to obtain API keys, cookies, and OAuth credentials, ensuring a smooth path from zero to a functioning personal data hub.
Use Case
Use this MCP server to centralize access to personal data across multiple platforms for AI assistants. The server exposes a comprehensive suite of tools that let you query and analyze data such as Steam libraries and achievements, YouTube video details and subscriptions, Bilibili watch history and favorites, Spotify user data and playlists, and Reddit activity and messaging. It supports automated token management (especially for YouTube), and provides integration guidance for running the server locally in various environments. Example usage from the docs:
{
"mcpServers": {
"personalhub": {
"command": "/absolute/path/to/your/project/venv/bin/python",
"args": ["/absolute/path/to/your/project/server.py"],
"env": {
"STEAM_API_KEY": "your_steam_api_key",
"STEAM_USER_ID": "your_steam_user_id",
"YOUTUBE_API_KEY": "your_youtube_api_key",
"BILIBILI_SESSDATA": "your_bilibili_sessdata",
"BILIBILI_BILI_JCT": "your_bilibili_bili_jct",
"BILIBILI_BUVID3": "your_bilibili_buvid3",
"REDDIT_CLIENT_ID": "your_reddit_client_id",
"REDDIT_CLIENT_SECRET": "your_reddit_client_secret"
}
}
}
}
# Test individual platforms
test_steam_credentials()
test_youtube_credentials()
test_bilibili_credentials()
test_reddit_credentials()
# Check overall status
get_personalization_status()
Available Tools (75)
Examples & Tutorials
Real usage patterns and code examples from the docs:
git clone https://github.com/YangLiangwei/PersonalizationMCP.git
cd PersonalizationMCP# Option A: Using conda (Recommended)
conda create -n personalhub python=3.12
conda activate personalhub
conda install lxml
pip install bilibili-api --no-deps
pip install -r requirements.txt # Option B: Using uv
uv venv
uv sync
source .venv/bin/activate
uv pip install lxml
uv pip install bilibili-api --no-deps
uv pip install aiohttp beautifulsoup4 colorama PyYAML brotli urllib3
# Option C: Using pip (Manual Multi-Step Installation)
python -m venv venv
source venv/bin/activate
pip install lxml
pip install bilibili-api --no-deps
pip install -r requirements.txt
{
"mcpServers": {
"personalhub": {
"command": "/path/to/your/conda/envs/personalhub/bin/python",
"args": ["/absolute/path/to/your/project/server.py"],
"env": {
"STEAM_API_KEY": "your_steam_api_key",
"STEAM_USER_ID": "your_steam_user_id",
"YOUTUBE_API_KEY": "your_youtube_api_key",
"BILIBILI_SESSDATA": "your_bilibili_sessdata",
"BILIBILI_BILI_JCT": "your_bilibili_bili_jct",
"BILIBILI_BUVID3": "your_bilibili_buvid3",
"REDDIT_CLIENT_ID": "your_reddit_client_id",
"REDDIT_CLIENT_SECRET": "your_reddit_client_secret"
}
}
}
}# Conda
conda activate personalhub
python server.py # uv
uv run python server.py
# pip with virtual environment
source venv/bin/activate # On Windows: venv\Scripts\activate
python server.py
# Test individual platforms
test_steam_credentials()
test_youtube_credentials()
test_bilibili_credentials()
test_reddit_credentials() # Check overall status
get_personalization_status()
Installation Guide
Step-by-step installation instructions with actual commands from the docs:
1) Quick Start
2) Install Dependencies (three options shown in the docs)
3) Configuration Setup
4) Cursor Configuration
Integration Guides
Frequently Asked Questions
Is this your MCP?
Claim ownership and get verified badge
Notes and cautions from the docs:
Prerequisites include: Python 3.12+ (as shown by the Python badge), obtaining API keys for Steam and YouTube, Bilibili cookies (sessdata, bili_jct, buvid3), Spotify client credentials, and Reddit client credentials. YouTube tokens are managed automatically via youtube_tokens.json; other tokens may require OAuth2 setup per platform.
Compare Alternatives
Similar MCP Tools
6 related toolsAnki MCP Server
A Model Context Protocol (MCP) server that enables AI assistants to interact with Anki, the spaced repetition flashcard application. The Anki MCP Server allows AI models to access Anki's card data, enabling features like automated flashcard creation, review, and management.
1MCP Agent
A unified Model Context Protocol server implementation that aggregates multiple MCP servers into one. The 1mcp-app/agent is an open-source project that provides a single entry point for multiple MCP servers, making it easier to manage and interact with various AI models and tools.
Roundtable AI MCP Server
Roundtable AI MCP Server is a zero-configuration local MCP server that unifies multiple AI coding assistants (Codex, Claude Code, Cursor, Gemini) through intelligent auto-discovery and a standardized interface. It coordinates specialized sub-agents from within your IDE to solve engineering problems in parallel, sharing context and synthesizing responses into a single, high-quality output. This documentation details installation, available MCP tools, integration with popular IDEs, and a broad ecosystem of specialized tools and CLIs that can be invoked as part of a roundtable-powered workflow, enabling developers to delegate tasks to the right AI for each facet of a problem without leaving their development environment.
MCPJungle
MCPJungle is a self-hosted MCP Gateway and Registry for AI agents. It serves as a central registry and gateway to manage Model Context Protocol (MCP) servers and the tools they expose. By consolidating MCP server registration, tool discovery, and access control, MCPJungle enables AI agents and clients to discover, group, and securely invoke tools from a single, unified gateway. The project provides a CLI, Docker-based deployment options, and enterprise-ready features such as tool grouping, access control, and observability to streamline MCP-based workflows across organizations.
mcpmcp-server
mcpmcp-server is a focused solution for discovering, setting up, and integrating MCP servers with your favorite clients to unlock AI-powered workflows. It streamlines how you connect MCP-powered servers to popular clients, enabling seamless AI-assisted interactions across your daily tools. The project emphasizes an approachable, config-driven approach to linking MCP servers with clients like Claude Desktop, while directing you to the homepage for variations across apps and platforms. This README highlights a practical JSON configuration example and notes on supported environments, helping you get started quickly and confidently.
Imagen3-MCP
Imagen3-MCP is an image generation service based on Google's Imagen 3.0 that exposes its functionality through MCP (Model Control Protocol). The project provides a server to run a local MCP service that accesses Google Gemini-powered image generation, enabling developers to integrate advanced image synthesis into their applications. The documentation covers prerequisites (Gemini API key), installation steps for Cherry Studio, and a Cursor-based JSON configuration example for embedding the MCP server in broader tooling. This MCP is designed to be deployment-friendly, with configurable environment variables and optional proxy settings to adapt to various network environments.