MCP Context Provider

🚀 MCP Context Provider

A static MCP (Model Context Protocol) server that provides AI models with persistent tool context, preventing context loss between chat sessions.

🚀 Quick Start

Option 1: Automated Installation (Recommended)

The easiest way to install MCP Context Provider is using the provided installation scripts:

Unix/Linux/macOS:

# Download the DXT package
wget https://github.com/doobidoo/MCP-Context-Provider/raw/main/mcp-context-provider-1.1.0.dxt

# Run the installation script
curl -sSL https://raw.githubusercontent.com/doobidoo/MCP-Context-Provider/main/install.sh | bash

Windows:

# Download and run the Windows installer
Invoke-WebRequest -Uri "https://raw.githubusercontent.com/doobidoo/MCP-Context-Provider/main/install.bat" -OutFile "install.bat"
.\install.bat

The installation script automatically:

• Unpacks the DXT extension
• Creates a Python virtual environment
• Installs all required dependencies
• Configures Claude Desktop settings

Option 2: Manual Installation from DXT

# Install DXT CLI (if not already installed)
npm install -g @anthropic-ai/dxt

# Download the DXT package
wget https://github.com/doobidoo/MCP-Context-Provider/raw/main/mcp-context-provider-1.1.0.dxt

# Unpack the extension to your desired location
dxt unpack mcp-context-provider-1.1.0.dxt ~/mcp-context-provider

# Navigate to the installation directory
cd ~/mcp-context-provider

# Create and activate a Python virtual environment
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

# Install dependencies
pip install mcp>=1.9.4

Option 3: Installation from Source

# Clone the repository
git clone https://github.com/doobidoo/MCP-Context-Provider.git
cd MCP-Context-Provider

# Create and activate a Python virtual environment
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

# Install dependencies
pip install -r requirements.txt

2. Configuration

Update your Claude Desktop configuration file:

Configuration File Location:

• Linux/Mac: ~/.config/claude/claude_desktop_config.json
• Windows: %APPDATA%\Claude\claude_desktop_config.json

For Virtual Environment Installation (Recommended):

{
  "mcpServers": {
    "context-provider": {
      "command": "/path/to/mcp-context-provider/venv/bin/python",
      "args": ["/path/to/mcp-context-provider/context_provider_server.py"],
      "env": {
        "CONTEXT_CONFIG_DIR": "/path/to/mcp-context-provider/contexts",
        "AUTO_LOAD_CONTEXTS": "true"
      }
    }
  }
}

For System Python Installation:

{
  "mcpServers": {
    "context-provider": {
      "command": "python",
      "args": ["context_provider_server.py"],
      "cwd": "/path/to/MCP-Context-Provider",
      "env": {
        "CONTEXT_CONFIG_DIR": "./contexts",
        "AUTO_LOAD_CONTEXTS": "true"
      }
    }
  }
}

Important: Replace /path/to/mcp-context-provider with the actual installation path.

3. Verify Installation

Run the verification script to ensure everything is configured correctly:

python verify_install.py

4. Restart Claude Desktop

After updating the configuration, restart Claude Desktop to load the MCP server.

✨ Features

• 🔄 Persistent Context: Rules and preferences survive across Claude Desktop restarts.
• ⚡ Automatic Injection: Context flows seamlessly into every conversation.
• 🎯 Tool-Specific: Each tool gets its own context rules and syntax preferences.
• 🔧 Auto-Corrections: Automatic syntax transformations ensure consistency across all interactions.
• 📈 Scalable: Easy to add new tools and context rules.
• 🏢 Enterprise-Ready: Version-controlled context management provides organizational stability.

📚 Documentation

Overview

The Context Provider acts as a persistent neural core for your AI interactions, eliminating the need to re-establish context in each new chat session.

The Neural Network Metaphor

Just like the image depicts, your MCP Context Provider functions as:

• Central Orb: The stable, persistent context core that maintains consistency.
• Neural Pathways: Tool-specific context rules that create specialized knowledge channels.
• Data Streams: Individual chat sessions that flow through and benefit from the persistent context.
• Network Stability: Prevents the ephemeral nature of conversations from losing important contextual information.

How It Works

Architecture

1. Context Provider Server: Python MCP server that loads JSON context files.
2. Context Files: Tool-specific rules stored in /contexts directory.
3. Claude Desktop Integration: MCP server registered in configuration.
4. Automatic Loading: Context is injected at startup and persists across chats.

Context Flow

Startup → Load Context Files → Register MCP Tools → Context Available in All Chats

Available Tools

Once loaded, the following tools are available in all chat sessions:

• get_tool_context: Get context rules for specific tool.
• get_syntax_rules: Get syntax conversion rules.
• list_available_contexts: List all loaded context categories.
• apply_auto_corrections: Apply automatic syntax corrections.

Context Files

The server loads context files from the /contexts directory:

• dokuwiki_context.json: DokuWiki syntax rules and preferences.
• terraform_context.json: Terraform naming conventions and best practices.
• azure_context.json: Azure resource naming and compliance rules.
• git_context.json: Git commit conventions and workflow patterns.
• general_preferences.json: Cross-tool preferences and standards.

Context File Structure

Each context file follows this pattern:

{
  "tool_category": "toolname",
  "description": "Tool-specific context rules",
  "auto_convert": true,
  "syntax_rules": {
    "format_rules": "conversion patterns"
  },
  "preferences": {
    "user_preferences": "settings"
  },
  "auto_corrections": {
    "regex_patterns": "automatic fixes"
  },
  "metadata": {
    "version": "1.0.0",
    "applies_to_tools": ["tool:*"]
  }
}

Examples

DokuWiki Syntax Conversion

Input (Markdown):

# My Header
This is `inline code` and here's a [link](http://example.com).

Auto-converted to DokuWiki:

====== My Header ======
This is ''inline code'' and here's a [[http://example.com|link]].

Azure Resource Naming

Input: storage_account_logs_prod Auto-corrected to: stlogsprod (following Azure naming conventions)

Git Commit Messages

Input: Fixed the login bug Auto-corrected to: fix: resolve login authentication issue

Adding New Context

To add support for a new tool:

1. Create a new JSON file: contexts/{toolname}_context.json.
2. Follow the standard context structure.
3. Restart Claude Desktop to load the new context.

The server automatically detects and loads any *_context.json files in the contexts directory.

Benefits

For Developers

• No need to re-establish context in new chats.
• Automatic syntax corrections save time.
• Consistent formatting across all work.
• Best practices automatically applied.

For Teams

• Shared context rules across team members.
• Version-controlled standards.
• Consistent code and documentation formatting.
• Enterprise compliance automatically enforced.

For Organizations

• Centralized context management.
• Scalable across multiple tools.
• Audit trail of context changes.
• Easy deployment and updates.

Advanced Usage

Custom Context Rules

Create your own context files by following the established pattern. The server supports:

• Regex-based auto-corrections.
• Tool-specific preferences.
• Conditional formatting rules.
• Multi-tool context inheritance.

Environment-Specific Context

Use environment variables to load different context sets:

{
  "env": {
    "CONTEXT_CONFIG_DIR": "./contexts/production",
    "ENVIRONMENT": "prod"
  }
}

Troubleshooting

Common Issues

1. Context not loading: Check file path in Claude Desktop config.
2. Server not starting: Verify Python dependencies installed.
3. Rules not applying: Check JSON syntax in context files.

See TROUBLESHOOTING.md for detailed solutions.

DXT Package Distribution

The MCP Context Provider is available as a Desktop Extension (DXT) package for easy distribution and installation:

• Package: mcp-context-provider-1.0.0.dxt (18.6 MB).
• Contents: Complete server with all dependencies bundled.
• Platform: Windows, macOS, Linux with Python 3.8+.
• Dependencies: Self-contained (no external pip requirements).

Building DXT Package

# Install DXT CLI
npm install -g @anthropic-ai/dxt

# Build the package
cd dxt
dxt pack

# The package will be created as mcp-context-provider-1.0.0.dxt

Distribution Notes

• The DXT package includes all Python dependencies (MCP SDK, Pydantic, etc.).
• Total unpacked size: ~45 MB including all dependencies.
• Optimized for offline installation and deployment.
• Compatible with corporate environments and air-gapped systems.

🔧 Technical Details

Context Files

The server loads context files from the /contexts directory. Each context file follows a specific JSON structure that includes tool category, description, auto-conversion settings, syntax rules, preferences, auto-corrections, and metadata.

Auto-corrections

The server uses regex patterns in the context files to apply automatic syntax corrections, ensuring consistency across all interactions.

Scalability

Adding new tools and context rules is easy, as the server automatically detects and loads any *_context.json files in the contexts directory.

🤝 Contributing

1. Fork the repository.
2. Create a feature branch: git checkout -b feature/new-context.
3. Add your context file to /contexts.
4. Test with your Claude Desktop setup.
5. Submit a pull request.

📄 License

MIT License - see LICENSE file for details.