Can I use only some platforms with PersonalizationMCP?

Yes. The documentation states you can configure only the platforms you want to use; missing credentials won’t cause errors.

How is YouTube OAuth2 token management handled?

The system implements automatic YouTube OAuth2 token management. Tokens are automatically detected and refreshed, and you don’t need to add an explicit YOUTUBE_ACCESS_TOKEN in your config; tokens are read from youtube_tokens.json.

What are the basic commands to run the MCP server?

You can run the server via Conda: conda activate personalhub; python server.py. Using uv: uv run python server.py. Using a pip environment: source venv/bin/activate; python server.py.

Back to Directory

PersonalizationMCP

Featured

MCP Personalization OAuth2 Steam YouTube Bilibili Spotify Reddit Cursor CLI

View Source Visit Website

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:

Quick Start: clone and run the server

git clone https://github.com/YangLiangwei/PersonalizationMCP.git

cd PersonalizationMCP

python server.py (or uv run python server.py depending on setup)

Installation options demonstrated in the docs:

Option A (Conda):

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 (uv):

uv venv

uv sync

source .venv/bin/activate

uv pip install lxml

Option C (pip):

python -m venv venv

source venv/bin/activate

pip install lxml

pip install bilibili-api --no-deps

pip install -r requirements.txt

Cursor configuration examples (environment setup) are shown as JSON blocks, e.g.:

{
"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"
}
}
}
}

Testing configuration examples:

# Test individual platforms
test_steam_credentials()
test_youtube_credentials()
test_bilibili_credentials()
test_reddit_credentials()

# Check overall status
get_personalization_status()

YouTube token management is automated: tokens are read/ refreshed from youtube_tokens.json; there is no need to manually add YOUTUBE_ACCESS_TOKEN in config.

Available Tools (75)

Examples & Tutorials

Real usage patterns and code examples from the docs:

Quick Start (clone and run):

git clone https://github.com/YangLiangwei/PersonalizationMCP.git
  cd PersonalizationMCP

Installation options (from the docs):

# 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

Cursor Configuration (examples shown in the docs):

{
    "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"
        }
      }
    }
  }

Running the server (Development section):

# 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

Testing configuration (from the docs):

# 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

git clone https://github.com/YangLiangwei/PersonalizationMCP.git

cd PersonalizationMCP

See detailed installation instructions: Installation and Setup (link in docs)

2) Install Dependencies (three options shown in the docs)

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 # On Windows: .venv\Scripts\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 # On Windows: venv\Scripts\activate

pip install lxml

pip install bilibili-api --no-deps

pip install -r requirements.txt

3) Configuration Setup

cp config.example config

Edit config with your credentials as shown in the docs.

4) Cursor Configuration

See JSON examples in Cursor Configuration section of the docs.

Integration Guides

Frequently Asked Questions

Is this your MCP?

Claim ownership and get verified badge

Repository Stats

Compare Alternatives

PersonalizationMCP

Social Content MCP Server

Prop Firm Deal Finder

Compare →

PersonalizationMCP

mcp-server-with-spring-ai

Similar MCP Tools

9 related tools

Social Content MCP Server

MCP server for social media and content platform data. Extract Reddit posts and comments, discover podcast episodes, and monitor social media trends. Analyze community discussions and track content engagement across platforms. Built on Apify cloud with Streamable HTTP transport. Connect via: https://nexgendata--social-content-mcp-server.apify.actor/mcp

Skyvern MCP

Skyvern is the complete browser MCP for AI agents. 75+ tools for clicking, filling forms, extracting structured data, logging in with 2FA, uploading files, drag-and-drop, running JavaScript, inspecting network traffic, multi-tab browsing, and building reusable cached workflows. First workflow run uses AI; subsequent runs replay a cached script with zero LLM calls.

pageguard-mcp

pageguard-mcp is an MCP (Model Context Protocol) server that exposes PageGuard privacy compliance scanning as a set of tools for AI coding assistants. It enables seamless integration with Claude Code, Cursor, Windsurf, ChatGPT, and any MCP-compatible environment. The server supports local scans, live URL scans, and AI-generated privacy-related documents, helping developers identify tracking technologies, cookies, and third-party data processing, while also producing tailored privacy policies and compliance materials. With its straightforward MCP configuration and free local scanning capability, pageguard-mcp is designed to empower teams to maintain privacy compliance across their projects and websites.

Prop Firm Deal Finder

Prop Firm Deal Finder (PFDF) is a free MCP server that gives AI assistants real-time access to live discount codes across 20+ proprietary trading firms. It provides 6 tools: get_deals (all current discounts), search_firms (search by name/asset class), compare_firms (side-by-side comparison of challenges), find_cheapest (cheapest challenge by account size with PFDF code applied), get_firm_details (15+ data points per firm), and get_discount_code (universal PFDF code). Covers firms like Topstep, Apex Trader Funding, MyFundedFutures, FTMO, The Funded Trader, and 14 more. No API key required. Install via npx propfirmdealfinder-mcp-server or connect remotely at https://web-production-6607c.up.railway.app/mcp

mcp-server-with-spring-ai

mcp-server-with-spring-ai is a Spring Boot integrated MCP (Model Context Protocol) server example that showcases how to expose executable tools from an MCP server to clients (including LLMs) and how to wire a MCP client to consume those tools. The documentation explains MCP at a high level, outlines the three-layer MCP Java SDK architecture (Client/Server Layer, Session Layer, Transport Layer), and demonstrates two sample tools implemented in SellerAccountTools. This repo emphasizes how an MCP server can connect to external data sources (e.g., a PostgreSQL DB) and expose tools that an AI model can invoke to retrieve data, with the example illustrating tool invocation and automatic tool selection by prompts.

OpenClaw MCP Server

OpenClaw MCP Server is a secure Model Context Protocol (MCP) bridge that connects Claude.ai with a self-hosted OpenClaw assistant, enabling OAuth2 authentication and safe, controlled communication between the Claude AI ecosystem and your local or hosted OpenClaw deployment. This MCP server acts as an orchestration layer that exposes MCP tools to Claude.ai, manages authentication, and enforces security boundaries like CORS and transport options. It is designed to be deployed via Docker or run locally, with detailed installation, configuration, and security guidance provided in the documentation. By serving as a bridge, it enables Claude.ai to delegate tasks to your OpenClaw bot while preserving control over data flow and access controls, in line with MCP specifications and best security practices.

Graphiti MCP Server

Graphiti MCP Server is an experimental implementation that exposes Graphiti's real-time, temporally-aware knowledge graph capabilities through the MCP (Model Context Protocol) interface. It enables AI agents and MCP clients to interact with Graphiti's knowledge graph for structured extraction, reasoning, and memory across conversations, documents, and enterprise data. The server supports multiple backends (FalkorDB by default and Neo4j), a variety of LLM providers (OpenAI, Anthropic, Gemini, Groq, Azure OpenAI), and multiple embedder options, all accessible via an HTTP MCP endpoint at /mcp/ for broad client compatibility. It also includes queue-based asynchronous episode processing, rich entity types for structured data, and flexible configuration through config.yaml, environment variables, or CLI arguments.

Context7 MCP Server

Context7 MCP Server delivers up-to-date, code-first documentation and examples for LLMs and AI code editors by pulling content directly from the source. It supports multiple MCP clients and exposes tools that help you resolve library IDs and retrieve library documentation, ensuring prompts use current APIs and usage patterns. The repository provides installation and integration guides for Cursor, Claude Code, Opencode, and other clients, along with practical configuration samples and OAuth options for remote HTTP connections. This MCP server is designed to keep prompts in sync with the latest library docs, reducing hallucinations and outdated code snippets.

TrendRadar MCP

TrendRadar MCP is an AI-driven Model Context Protocol (MCP) based analysis server that exposes a suite of specialized tools for cross-platform news analysis, trend tracking, and intelligent push notifications. It integrates with TrendRadar’s multi-platform data aggregation (RSS and trending topics) and provides advanced AI-powered insights, sentiment analysis, and cross-platform correlation. The MCP server enables developers to query, analyze, and compare news across platforms using a consistent toolset, with ongoing updates that expand capabilities such as RSS querying, date parsing, and multi-date trend analysis. This documentation references the MCP module updates, tool additions, and architecture changes that enhance extensibility, cross-platform data handling, and AI-assisted reporting.

PersonalizationMCP

PersonalizationMCP Details

Use Case

Available Tools (75)

get_steam_library()

get_steam_recent_activity()

get_steam_friends()

get_steam_profile()

get_player_achievements(app_id)

get_user_game_stats(app_id)

get_friends_current_games()

compare_games_with_friend(friend_steamid)

get_friend_game_recommendations(friend_steamid)

search_youtube_videos(query)

get_video_details(video_id)

get_channel_info(channel_id)

get_trending_videos()

get_youtube_subscriptions()

get_youtube_playlists()

get_youtube_liked_videos()

refresh_youtube_token()

get_youtube_token_status()

get_bilibili_user_info(uid)

get_my_bilibili_profile()

search_bilibili_videos(keyword)

get_bilibili_video_info(bvid)

get_bilibili_user_videos(uid)

get_bilibili_following_list()

get_bilibili_watch_history()

get_bilibili_favorites()

get_bilibili_liked_videos()

get_bilibili_coin_videos()

get_bilibili_toview_list()

test_spotify_credentials()

setup_spotify_oauth()

complete_spotify_oauth()

get_spotify_token_status()

refresh_spotify_token()

get_current_user_profile()

get_user_top_items()

get_user_recently_played()

get_followed_artists()

follow_artists_or_users()

unfollow_artists_or_users()

get_user_saved_tracks()

get_user_saved_albums()

get_user_saved_shows()

get_user_saved_episodes()

get_current_user_playlists()

get_playlist_items()

test_reddit_credentials()

setup_reddit_oauth()

complete_reddit_oauth()

get_reddit_token_status()

refresh_reddit_token()

auto_refresh_reddit_token_if_needed()

get_user_subreddits()

get_user_trophies()

get_user_preferences()

get_user_karma_breakdown()

get_moderated_subreddits()

get_contributor_subreddits()

get_user_submitted_posts()

get_user_comments()

get_user_overview()

get_saved_content()

get_hidden_posts()

get_upvoted_content()

get_downvoted_content()

get_inbox_messages()

get_unread_messages()

get_sent_messages()

test_connection()

get_personalization_status()

test_steam_credentials()

test_youtube_credentials()

test_bilibili_credentials()

test_spotify_credentials()

test_reddit_credentials()

Examples & Tutorials