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Personal Context Technology MCP Server

by mikhashev

Personal Context Technology MCP Server

This repository implements a server for Personal Context Technology (PCT) using the Model Context Protocol (MCP). It enables AI assistants like Claude to access and update your personalized context data, creating persistent memory between sessions.

What is Personal Context Technology?

Personal Context Technology allows you to structure and manage personal data that AI assistants can use to provide more personalized and relevant responses. With PCT:

  • Your context is preserved between AI sessions

  • You control where your data is stored (locally, cloud, etc.)

  • You define explicit instructions for how AI systems should use your data

  • You can update context as your preferences and situation change

Related MCP server: Recall

Features

  • Persistent Context Storage: Store your preferences, goals, learning style, and other personal information

  • Privacy Controls: Instruction block defines which information is private/public

  • Section-based Access: Access full context or individual sections

  • Context Updates: Update specific fields via the updateContext tool

  • Version Tracking: Maintain history of context changes

Demo

Watch the Personal Context Technology MCP Server in action:

PCT MCP Server Demo

The demo shows how to:

  • Access personal context data using the MCP tool

  • Update context information using the updateContext tool

  • See how Claude's responses improve with personalized context

Installation

Prerequisites

  • Node.js (v16+)

  • npm (included with Node.js)

Setup Instructions

  1. Clone the repository

git clone https://github.com/mikhashev/pct-mcp-server.git
cd pct-mcp-server
  1. Install dependencies

npm install
  1. Build the server

npm run build
  1. Start the server

npm start

Connecting to Claude Desktop

To use this server with Claude Desktop:

Windows

  1. Open Claude Desktop

  2. Navigate to Settings > Developer > Edit Config

  3. This will open %AppData%\Claude Desktop\ directory

  4. Create or edit claude_desktop_config.json with the following content:

{
  "mcpServers": {
    "personal-context-server": {
      "command": "node",
      "args": ["PATH_TO_YOUR_PROJECT/pct-mcp-server/dist/index.js"],
      "env": {}
    }
  }
}

Replace PATH_TO_YOUR_PROJECT with the actual path to your project.

macOS

  1. Open Claude Desktop

  2. Navigate to Settings > Developer > Edit Config

  3. This will open ~/Library/Application Support/Claude Desktop/ directory

  4. Create or edit claude_desktop_config.json with the following content:

{
  "mcpServers": {
    "personal-context-server": {
      "command": "node",
      "args": ["/path/to/your/project/pct-mcp-server/dist/index.js"],
      "env": {}
    }
  }
}

Linux

  1. Open Claude Desktop

  2. Navigate to Settings > Developer > Edit Config

  3. This will open ~/.config/Claude Desktop/ directory

  4. Create or edit claude_desktop_config.json with the following content:

{
  "mcpServers": {
    "personal-context-server": {
      "command": "node",
      "args": ["/path/to/your/project/pct-mcp-server/dist/index.js"],
      "env": {}
    }
  }
}

Using the Personal Context Server

With Claude Desktop

  1. Start your PCT MCP server using npm start

  2. Restart Claude Desktop (if needed)

  3. In Claude, click the MCP tool icon (wrench/tool icon) in the input field

  4. You'll see "personal-context-server" with available tools and resources:

    • Resources: Access your context data (full context or by section)

    • Tools: Update specific fields in your context

Accessing Context Data

Claude can access your personal context data in two ways:

  1. Full Context: Select "All Sections" to give Claude access to your entire personal context

  2. Section-based: Select specific sections (basic_info, preferences, etc.) to share only certain parts of your context

Example prompt: "Using my context data, recommend some learning resources that would match my learning style."

Updating Context Data

You can update your personal context using the updateContext tool:

Example prompt: "Update my learning_style in preferences to 'visual and hands-on' because I've found I learn better with practical examples."

Customizing Your Context

The default personal context includes:

  • basic_info: Name, location, etc.

  • preferences: Communication style, learning preferences, etc.

  • instruction: Rules for how AI should use and update your context

  • metadata: Version tracking, update history

You can modify the default structure by editing the DEFAULT_CONTEXT object in src/storage/contextStorage.ts. Or just replace update file data [PATH_TO_YOUR_PROJECT/pct-mcp-server/data/personal_context.json] from template data in repo (https://github.com/mikhashev/personal-context-manager/blob/main/use-cases/self-education/personal_context_self_education_template.json) or another one.

Project Structure

pct-mcp-server/
├── .git/                      # Git repository data
├── .gitignore                 # Git ignore rules
├── LICENSE                    # License file
├── README.md                  # Project documentation
├── package-lock.json          # Lock file for npm dependencies 
├── package.json               # Project configuration and dependencies
├── tsconfig.json              # TypeScript configuration
└── src/                       # Source code
    ├── index.ts               # Main application entry point
    ├── resources/             # MCP resources implementation
    │   └── personalContext.ts # Personal context resource
    ├── storage/               # Storage implementation
    │   └── contextStorage.ts  # Context storage functionality
    ├── tools/                 # MCP tools implementation
    │   ├── contextSuggestion.ts  # Context suggestion tool
    │   └── updateContext.ts   # Update context tool
    └── utils/                 # Utility functions
        └── instructionHandler.ts  # Instruction handling logic

# Generated directories (not in repository)
# ----------------------------------------
# dist/                        # Compiled JavaScript files (gitignored)
# node_modules/                # Dependencies (gitignored)
# data/                        # User data (gitignored or contains only samples)
#   └── personal_context.json  # Personal context data (sample only) (don't change filename personal_context.json, update data if need)

Advanced Configuration

Using HTTP Transport

The default implementation uses stdio transport for development. For production use, you can switch to HTTP transport:

  1. Edit src/index.ts

  2. Uncomment the HTTP transport section

  3. Configure your desired port and CORS settings

  4. Rebuild and restart the server

! I reccomend use stdio transport for development locally on your machine always.

Multi-User Support

To support multiple users, modify the contextStorage.js file to store contexts in user-specific files based on user IDs.

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

License

This project is released under the MIT License - See LICENSE that covers the project.

Acknowledgments

This project is based on the Personal Context Technology concept and implements the Model Context Protocol (MCP) developed by Anthropic to enable AI systems to access external context.

Available Tools

1 tool
updateContextD
ParametersJSON Schema
NameRequiredDescriptionDefault
pathYesPath to the field to update (e.g., 'preferences.learning_style')
valueYesNew value for the field
reasonYesReason for the update

TDQS

D1/5.0
Behavior1/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Tool has no description.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness1/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Tool has no description.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness1/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Tool has no description.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters1/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Tool has no description.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose1/5

Does the description clearly state what the tool does and how it differs from similar tools?

Tool has no description.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines1/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Tool has no description.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

TDQS

D1.2/5.0
Disambiguation1/5

Only one tool exists, so there is no ambiguity among tools. However, the lack of description means it's impossible to understand its purpose, making selection meaningless.

Naming Consistency3/5

The single tool uses camelCase. With only one tool, consistency cannot be evaluated meaningfully, so a neutral score is given.

Tool Count1/5

A single tool with no description is far too few for a meaningful server. It suggests minimal utility and incomplete implementation.

Completeness1/5

With only one tool and no description, the tool surface is severely incomplete. The server's purpose is unknown, and no workflows can be supported.

Maintenance

ActivityInactive
ResponsivenessSyncing

Resources

Unclaimed servers have limited discoverability.

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