MCP Comparison
Compare features, tools, and capabilities of these MCP servers side by side.
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.
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.
| Feature | Graphiti MCP Server | PersonalizationMCP |
|---|---|---|
| Verified | ||
| Official | ||
| Tools Available | 9 | 75 |
| Has Installation Guide | ||
| Has Examples | ||
| Website | ||
| Source Code |
- add_episode
- search_nodes
- search_facts
- delete_entity_edge
- delete_episode
- get_entity_edge
- get_episodes
- clear_graph
- get_status
- 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
Can't decide? Check out both MCP servers for more details.