MCP Context Provider

The stable, glowing orb at the center represents the persistent context that survives across chat sessions. The flowing data streams show how ongoing conversations connect to and draw from this stable core of information, preventing context loss.

A static MCP (Model Context Protocol) server that provides AI models with persistent tool context, preventing context loss between chat sessions. This server automatically loads and injects tool-specific rules, syntax preferences, and best practices at Claude Desktop startup.

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 by:

🔄 Persistent Context: Like the stable orb in the visualization, rules and preferences survive across Claude Desktop restarts
⚡ Automatic Injection: Context flows seamlessly into every conversation, just as the data streams connect to the central core
🎯 Tool-Specific: Each tool gets its own context rules and syntax preferences, creating specialized knowledge pathways
🔧 Auto-Corrections: Automatic syntax transformations (e.g., Markdown → DokuWiki) ensure consistency across all interactions
📈 Scalable: Easy to add new tools and context rules, expanding the knowledge network
🏢 Enterprise-Ready: Version-controlled context management provides organizational stability

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

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.

How It Works

Architecture

Context Provider Server: Python MCP server that loads JSON context files
Context Files: Tool-specific rules stored in /contexts directory
Claude Desktop Integration: MCP server registered in configuration
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

Screenshot showing the MCP Context Provider in action within Claude Desktop. The tool automatically detects and lists all available context categories (dokuwiki, terraform, azure, git, general_preferences) and provides interactive access to tool-specific rules and guidelines.

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:

Create a new JSON file: contexts/{toolname}_context.json
Follow the standard context structure
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

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

See TROUBLESHOOTING.md for detailed solutions.

Documentation

Context Guide: Complete context file reference
Developer Guide: Creating custom contexts
Examples: Real-world usage examples
Troubleshooting: Common issues and 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

To build your own DXT package from source:

# 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

Contributing

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

License

MIT License - see LICENSE file for details.

This server cannot be installed

security - not tested

license - permissive license

quality - not tested

How are these scores calculated?

local-only server

The server can only run on the client's local machine because it depends on local resources.

Provides persistent tool context that survives across Claude Desktop chat sessions, automatically injecting tool-specific rules, syntax preferences, and best practices. Eliminates the need to re-establish context in each new conversation.

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