MCP Comparison
Compare features, tools, and capabilities of these MCP servers side by side.
PersonalizationMCP
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.
Figma MCP server
The Figma MCP server enables design context delivery from Figma files to AI agents and code editors, empowering teams to generate code directly from design selections. It supports both a remote hosted server and a locally hosted desktop server, allowing seamless integration with popular editors through Code Connect and a suite of tools that extract design context, metadata, variables, and more. This guide covers enabling the MCP server, configuring clients (VS Code, Cursor, Claude Code, and others), and using a curated set of MCP tools to fetch structured design data for faster, more accurate code generation. It also explains best practices, prompts, and integration workflows that help teams align generated output with their design systems. The documentation includes concrete JSON examples for configuring servers in editors like VS Code and Cursor, as well as command examples for Claude Code integration and plugin installation.
| Feature | PersonalizationMCP | Figma MCP server |
|---|---|---|
| Verified | ||
| Official | ||
| Tools Available | 75 | 8 |
| Has Installation Guide | ||
| Has Examples | ||
| Website | ||
| Source Code |
- 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)
- +65 more tools
- get_design_context
- get_variable_defs
- get_code_connect_map
- get_screenshot
- create_design_system_rules
- get_metadata
- get_figjam
- whoami
Can't decide? Check out both MCP servers for more details.