What is Mcp You

mcp-you is a Model Context Protocol (MCP) server designed to serve as a memory store and personality guide for Large Language Models (LLMs), enabling them to emulate specific individuals by utilizing personalized memories and characteristics.

Use cases

Use cases for mcp-you include creating virtual companions that remember user preferences, enhancing customer service bots with personalized responses, and developing educational applications that adapt to individual learning styles.

How to use

To use mcp-you, configure the paths for the personality profile and database in the server settings. Load a ‘personality.json’ file containing the user’s traits, communication style, and background, and interact with the server to store and retrieve memories.

Key features

Key features of mcp-you include memory persistence across server restarts using SQLite, dynamic loading of personality profiles from JSON files, and the ability to guide LLM responses based on detailed personality insights.

Where to use

mcp-you can be used in various fields such as personalized AI assistants, therapeutic chatbots, educational tools, and any application requiring nuanced human-like interactions.

Personality & Memory MCP Server

A Model Context Protocol (MCP) server designed to act as a memory store and personality guide for a Large Language Model (LLM). The goal is to help the LLM behave, think, and act more like a specific person by providing it with relevant memories and personality characteristics.

This server loads a comprehensive personality profile dynamically from a personality.json file (path configurable via PERSONALITY_JSON_PATH). Memories are persisted to an SQLite database (path configurable via DATABASE_PATH, defaults to mcp_memory.sqlite in the project root).

Purpose

This server allows an LLM to:

• Store and retrieve memories (events, facts, journal entries, feelings) with persistence across server restarts (using an SQLite database).
• Access detailed information about a user’s personality, background, interests, social style, cognitive profile, and significant experiences (all loaded from personality.json).
• Receive guidance on how to respond to situations, tell stories, or craft messages in a way that aligns with the user’s nuanced persona.

personality.json Structure

The personality.json file should be placed in the root of the project and follow this structure (examples are illustrative):

{
  "traits": [
    "Curious and inquisitive",
    "Empathetic and understanding"
  ],
  "communicationStyle": {
    "preferences": [
      "Prefers clear and direct communication"
    ],
    "commonPhrases": [
      "That's interesting",
      "Could you elaborate?"
    ],
    "tone": "Generally calm and thoughtful"
  },
  "valuesAndBeliefs": {
    "coreValues": [
      "Honesty and integrity"
    ],
    "philosophicalLeanings": "Stoicism-inspired pragmatism"
  },
  "background": {
    "demographics": {
      "birthDate": "1990-01-01",
      "nationality": "Canadian",
      "culturalBackground": "Mixed heritage",
      "hometown": "Vancouver, BC",
      "currentLocation": "Toronto, ON"
    },
    "education": {
      "highestLevel": "Master's Degree",
      "fieldOfStudy": "Computer Science",
      "almaMater": "University of Example",
      "keyExperiences": [
        "Thesis on natural language processing"
      ]
    },
    "profession": {
      "occupation": "AI Researcher",
      "industry": "Technology",
      "yearsOfExperience": 8,
      "keySkills": [
        "Python",
        "TensorFlow",
        "Cloud Computing"
      ],
      "careerHighlights": [
        "Published a paper at NeurIPS"
      ],
      "careerGoals": "Lead an AI ethics research team",
      "workStyle": "Collaborative, focused on deep work"
    }
  },
  "interestsAndPreferences": {
    "hobbiesAndPastimes": {
      "mainHobbies": [
        "Reading philosophy",
        "Playing the piano"
      ],
      "otherInterests": [
        "Learning Japanese",
        "Urban gardening"
      ]
    },
    "sportsAndActivities": {
      "regular": [
        "Swimming"
      ],
      "occasional": [
        "Skiing"
      ]
    },
    "mediaPreferences": {
      "books": {
        "genres": [
          "Philosophy",
          "Classic Literature"
        ],
        "favoriteAuthors": [
          "Albert Camus",
          "Jane Austen"
        ]
      },
      "movies": {
        "genres": [
          "Foreign Films",
          "Psychological Thrillers"
        ],
        "favoriteDirectorsOrActors": [
          "Akira Kurosawa"
        ]
      },
      "music": {
        "genres": [
          "Classical",
          "Jazz"
        ],
        "favoriteArtistsOrBands": [
          "Miles Davis"
        ]
      },
      "podcastsOrTalks": [
        "Philosophy Bites podcast"
      ]
    },
    "tastesAndAesthetics": {
      "artistic": "Minimalism, Bauhaus architecture",
      "fashion": "Comfortable, minimalist",
      "food": {
        "dietaryPreferences": [],
        "likes": [
          "Sushi",
          "Artisanal bread"
        ],
        "dislikes": [
          "Fast food"
        ]
      },
      "travel": {
        "style": "Cultural immersion, historical sites",
        "dreamDestinations": [
          "Machu Picchu",
          "Rome"
        ]
      }
    }
  },
  "socialProfile": {
    "interactionStyle": "Prefers meaningful conversations over small talk",
    "familyValues": "Values family support and traditions",
    "friendshipApproach": "Values deep, long-lasting friendships",
    "keyPeopleInfluence": {
      "mentors": [
        "Dr. Eleanor Vance (PhD Advisor)"
      ],
      "roleModels": [
        "Simone de Beauvoir"
      ]
    }
  },
  "cognitiveEmotionalProfile": {
    "learningStyle": "Auditory and kinesthetic",
    "problemSolvingApproach": "Analytical, breaks problems into smaller parts",
    "stressResponse": "Seeks quiet time for reflection, practices mindfulness",
    "motivations": [
      "Intellectual challenge",
      "Contributing to knowledge"
    ],
    "fearsOrAnxietiesGeneral": [
      "Misinformation",
      "Lack of critical thinking"
    ],
    "senseOfHumorDetails": "Appreciates irony and subtle wit",
    "petPeeves": [
      "Loud chewing",
      "Unnecessary jargon"
    ]
  },
  "significantExperiencesSummary": {
    "formativeChildhood": [
      "First encounter with a computer sparked interest in tech"
    ],
    "majorLifeEvents": [
      "Presenting research at an international conference"
    ],
    "travelHighlights": [
      "Solo trip to Iceland"
    ],
    "proudestAccomplishments": [
      "Mentoring students who achieved their goals"
    ],
    "challengesOvercome": [
      "Learning to code proficiently from scratch"
    ]
  }
}

Implemented MCP Features

Resources

• memory://all (listAllMemories): Lists a summary of all stored memories.

• memory://id/{memoryId} (getMemoryById): Retrieves a specific memory by its unique ID.

• personality://traits (getPersonalityTraits): Provides a list of the user’s key personality traits.

• personality://communication_style (getCommunicationStyle): Details the user’s communication style (preferences, common phrases, tone).

• personality://values_beliefs (getValuesAndBeliefs): Outlines core values and philosophical leanings.

• personality://background/demographics (getBackgroundDemographics): User’s demographic information.

• personality://background/education (getBackgroundEducation): User’s educational background.

• personality://background/profession (getBackgroundProfession): User’s professional background.

• personality://interests/hobbies (getInterestsHobbies): User’s hobbies and pastimes.

• personality://interests/media (getInterestsMedia): User’s media preferences (books, movies, music, etc.).

• personality://interests/tastes (getInterestsTastes): User’s tastes and aesthetic preferences.

• personality://social (getSocialProfile): User’s social interaction style and influences.

• personality://cognitive_emotional (getCognitiveEmotionalProfile): User’s cognitive and emotional profile.

• personality://experiences_summary (getSignificantExperiencesSummary): Summaries of significant life experiences.

(All personality resources load their data from personality.json)

Tools

• addMemory: Adds a new memory to the store and saves it to the database.
  • Parameters:
    • content: string (The content of the memory)
    • type: string (e.g., ‘journal’, ‘event’, ‘fact’)
    • tags: string[] (List of tags)
    • timestamp?: string (ISO 8601 datetime, defaults to now)
• searchMemories: Searches stored memories.
  • Parameters:
    • query: string (Search term for content or tags)
    • relevance?: number (0-1, placeholder: sorts tag matches higher, full relevance scoring not yet implemented)
    • date_filter?: string (Filters memories by date. Supported formats: “YYYY-MM-DD”, “YYYY-MM”, “today”, “yesterday”, “last_7_days”, “last_30_days”)
• updatePersonalityField: Updates a field in personality.json and reloads the data in the server. This allows dynamic updates to the user’s persona.
  • Parameters:
    • fieldPath: string (Dot-separated path to the personality field, e.g., ‘traits’, ‘background.demographics.currentLocation’)
    • newValue: any (The new value for the field. Must match the expected structure of the target field. For arrays, provide the entire new array to replace.)

Prompts

• actAsUser: Provides a system prompt to guide the LLM to generally act in accordance with the user’s persona, drawing from loaded personality data.
  • Parameters: None
• respondToSituation: Guides the LLM to generate a response to a specific situation as the user might, considering persona and potentially memories.
  • Parameters:
    • situation_description: string (Description of the situation)
• tellStory: Guides the LLM to generate a story on a given topic, in the user’s style, incorporating memories and persona from loaded data.
  • Parameters:
    • topic: string (The topic for the story)
• briefOnUserPersona: Provides the LLM with a comprehensive briefing on the user’s persona (traits, communication style, values, background snippets, interests) and a few recent memories. This is intended to be used at the start of a conversation to help the LLM align with the user.
  • Parameters: None

Setup and Running

1. Prerequisites: Ensure you have Node.js and npm installed.

2. Create personality.json: In the root of the project (or at the path specified by the PERSONALITY_JSON_PATH environment variable), create a personality.json file following the structure outlined above. Populate it with the user’s information.

3. Database Setup: The SQLite database for memories will be created automatically (default: mcp_memory.sqlite in project root, configurable via DATABASE_PATH). No manual setup is needed for the database file itself initially.

4. Install Dependencies:

   npm install
   

5. Build the Server:

   npm run build
   

6. Run the Server:
   The server uses STDIO transport by default. You can set environment variables for file paths before running:

   # Example for Linux/macOS
   export PERSONALITY_JSON_PATH="/custom/path/to/your_personality.json"
   export DATABASE_PATH="/custom/path/to/your_memory_db.sqlite"
   npm start
   
   # Example for Windows (Command Prompt)
   set PERSONALITY_JSON_PATH="C:\custom\path\to\your_personality.json"
   set DATABASE_PATH="C:\custom\path\to\your_memory_db.sqlite"
   npm start
   

   You can then connect to this server from an MCP client like Claude Desktop by configuring it to run the start command (or node build/personality-memory-server.js directly if preferred, ensuring correct paths and environment variables are set for the client’s execution context if needed).

Future Enhancements

• More sophisticated memory search leveraging SQLite’s capabilities (relevance, date filters, full-text search).
• Persistent storage for memories (e.g., SQLite, JSON files).
• More granular error handling and improved default values if personality.json is incomplete.
• Ability to update personality.json via MCP tools (with appropriate security).